168 accepted papers
Simultaneous localization and mapping is a critical capability for autonomous systems. Traditional SLAM approaches often rely on visual or LiDAR sensors and face significant challenges in adverse conditions such as low light or featureless environments. To overcome these limitations, we propose a no…
Reliable incremental estimation of camera poses and 3D reconstruction is key to enable various applications including robotics, interactive visualization, and augmented reality. However, online SLAM is particularly challenging in dynamic natural environments, where scene dynamics can severely deteri
Robust robot navigation in outdoor environments requires accurate perception systems capable of handling visual challenges such as repetitive structures and changing appearances. Visual feature matching is crucial to vision-based pipelines but remains particularly challenging in natural outdoor sett…
Localization of autonomous vehicles in existing maps is crucial for reliable navigation. Using previously constructed maps allows vehicles to estimate their pose without the inherent odometry drift. Building such maps involves aligning data recorded at different times and maintaining the map over ti
The perception of moving objects is crucial for autonomous robots performing collision avoidance in dynamic environments. LiDARs and cameras tremendously enhance scene interpretation but do not provide direct motion information and face limitations under adverse weather. Radar sensors overcome these
Accurate odometry is a critical component in a robotic navigation stack, and subsequent modules such as planning and control often rely on an estimate of the robot’s motion. LiDAR-based odometry approaches should be robust across sensor types and deployable in different target domains, from solid-st
Abstract— Understanding the surrounding scene geometrically and semantically is a key requirement for autonomously navigating systems. Vision-based 3D panoptic occupancy prediction aims to provide a 3D representation of the surroundingsincluding semantic meaning and identifying individual objectssuc…
Crop yield estimation is a relevant problem in agriculture, because an accurate yield estimate can support farmers’ decisions on harvesting or precision intervention. Robots can help to automate this process. To do so, they need to be able to perceive the surrounding environment to identify target o
As the world population is expected to reach 10 billion by 2050, our agricultural production system needs to double its productivity despite a decline of human workforce in the agricultural sector. Autonomous robotic systems are one promising pathway to increase productivity by taking over labor-int
Robotics applications often rely on scene reconstructions to enable downstream tasks. In this work, we tackle the challenge of actively building an accurate map of an unknown scene using an RGB-D camera on a mobile platform. We propose a hybrid map representation that combines a Gaussian splatting m
Robots need to know where they are in the world to operate effectively without human support. One common first step for precise robot localization is visual place recognition. It is a challenging problem, especially when the output is required in an online fashion, and the current state-of-the-art a
Accurate localization is crucial for the autonomous operation of mobile robots. Specifically for indoor scenarios, localization algorithms typically rely on a previously generated map. However, many real-world sites like warehouses or healthcare environments violate the underlying assumption that th
Most autonomous cars rely on the availability of high-definition (HD) maps. Current research aims to address this constraint by directly predicting HD map elements from onboard sensors and reasoning about the relationships between the predicted map and traffic elements. Despite recent advancements,
Forests are vital to our ecosystems, acting as carbon sinks, climate stabilizers, biodiversity centers, and wood sources. Due to their scale, monitoring and managing forests takes a lot of work. Forestry robotics offers the potential for enabling efficient and sustainable foresting practices through
Simultaneous localization and mapping is a critical capability for autonomous systems. Traditional SLAM approaches often rely on visual or LiDAR sensors and face significant challenges in adverse conditions such as low light or featureless environments. To overcome these limitations, we propose a no
Odometry is crucial for the navigation of autonomous vehicles in unknown environments. While cameras and LiDARs are commonly used to estimate the ego-motion of a vehicle, these sensors face limitations under bad lighting and severe weather conditions. Automotive radars overcome these challenges, but
Globally localizing a mobile robot in a known map is often a foundation for enabling robots to navigate and operate autonomously. In indoor environments, traditional Monte Carlo localization based on occupancy grid maps is considered the gold standard, but its accuracy is limited by the representati
Robust and accurate localization and mapping of an environment using laser scanners, so-called LiDAR SLAM, is essential to many robotic applications. Early 3D LiDAR SLAM methods often exploited additional information from IMU or GNSS sensors to enhance localization accuracy and mitigate drift. Later
Robust robot navigation in outdoor environments requires accurate perception systems capable of handling visual challenges such as repetitive structures and changing appearances. Visual feature matching is crucial to vision-based pipelines but remains particularly challenging in natural outdoor sett
Robots require high-fidelity reconstructions of their environment for effective operation. Such scene representations should be both, geometrically accurate and photorealistic to support downstream tasks. While this can be achieved by building distance fields from range sensors and radiance fields f…
Simultaneous localization and mapping are essential components for the operation of autonomous vehicles in unknown environments. While localization focuses on estimating the vehicle's pose, mapping captures the surrounding environment to enhance future localization and decision-making. Localization
Semantic scene understanding, including the perception and classification of moving agents, is essential to enabling safe and robust driving behaviours of autonomous vehicles. Cameras and LiDARs are commonly used for semantic scene understanding. However, both sensor modalities face limitations in a
Semantic scene understanding is crucial for autonomous systems and 3D semantic occupancy prediction is a key task since it provides geometric and possibly semantic information of the vehicle's surroundings. Most existing vision-based approaches to occupancy estimation rely on 3D voxel labels or segm
Automatic fruit growth monitoring plays a vital role in advancing precision agriculture. Tracking the evolution of fruits over time is essential to monitor their development and optimize production. The ability to recognize fruits over periods of time, even with drastic scene changes, is a required
Robots are frequently tasked to gather relevant sensor data in unknown terrains. A key challenge for classical path planning algorithms used for autonomous information gathering is adaptively replanning paths online as the terrain is explored given limited onboard compute resources. Recently, learni
The natural world presents complex organic structures, such as tree canopies, that humans can interpret even when only partially visible.Understanding tree structures is key for forest monitoring, orchard management, and automated harvesting applications.However, reconstructing tree topologies from…
Conventional crop production, which is essential for providing food, feed, fuel, and fiber for our society, relies heavily on harmful herbicides to control weeds. Instead, agricultural robots could remove weeds more sustainably. However, these robots require a generalizable perception system that ca
Building accurate maps is a key building block to enable reliable localization planning and navigation of autonomous vehicles. We propose a novel approach for building accurate 3D maps of dynamic environments utilizing a sequence of LiDAR scans. To this end we propose encoding the 4D scene into a no…
Agricultural production is facing challenges in the next decades induced by climate change and the need for more sustainability by reducing its impact on the environment. Advances in field management through robotic intervention, monitoring of crops by autonomous unmanned aerial vehicles (UAVs) supp…
Autonomousrobots are often employed for data collection due to their efficiency and low labour costs. A key task in robotic data acquisition is planning paths through an initially unknown environment to collect observations given platform-specific resource constraints, such as limited battery life.
The ability to detect loop closures plays an essential role in any SLAM system. Loop closures allow correcting the drifting pose estimates from a sensor odometry pipeline. In this paper, we address the problem of effectively detecting loop closures in LiDAR SLAM systems in various environments with…
Robots that operate in agricultural environments need a robust perception system that can deal with occlusions, which are naturally present in agricultural scenarios. In this paper, we address the problem of estimating 3D shapes of fruits when only partial observations are available. Generally speak…
Object reconstruction is relevant for many autonomous robotic tasks that require interaction with the environment. A key challenge in such scenarios is planning view configurations to collect informative measurements for reconstructing an initially unknown object. One-shot view planning enables effi
Robust and accurate point cloud registration is an essential part of many robotic tasks such as SLAM or object pose retrieval. In this paper, we address the problem of global 3D point cloud registration, i.e., the task of estimating the 3D rigid body transform between a source and a target point clo…
Mobile robots increasingly operate in real-world environments that are subject to change over time. Accurate and robust localization is, however, crucial for the effective operation of autonomous mobile systems. In this letter, we tackle the challenge of developing a generalizable learned filter for
Moving object segmentation (MOS) using a 3D light detection and ranging (LiDAR) sensor is crucial for scene understanding and identification of moving objects. Despite the availability of various types of 3D LiDAR sensors in the market, MOS research still predominantly focuses on 3D point clouds fro
The world population is increasing and will, by 2050, nearly double its demand for food, feed, fuel, and fiber. Besides environmental challenges, labor shortage also poses crucial challenges to the agricultural production system. Automation of manual tasks in crop production can potentially increase
Basically all multi-sensor systems must calibrate their sensors to exploit their full potential for state estimation such as mapping and localization. In this letter, we investigate the problem of extrinsic and intrinsic calibration of perception systems. Traditionally, targets in the form of checke
Odometry estimation is crucial for every autonomous system requiring navigation in an unknown environment. In modern mobile robots, 3D LiDAR-inertial systems are often used for this task. By fusing LiDAR scans and IMU measurements, these systems can reduce the accumulated drift caused by sequentiall…
Maps are essential for diverse applications, such as vehicle navigation and autonomous robotics. Both require spatial models for effective route planning and localization. This paper addresses the challenge of road graph construction for autonomous vehicles. Despite recent advances, creating a road…
Interpreting camera data is key for autonomously acting systems such as autonomous vehicles. Vision systems that operate in real-world environments must be able to understand their surroundings and need the ability to deal with novel situations. This paper tackles open-world semantic segmentation i.…
Precise scene understanding is key for most robot monitoring and intervention tasks in agriculture. In this work we present <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PAg-NeRF</b> which is a novel NeRF-based system that enables 3D panoptic scene u
A detailed analysis of agricultural fields is key toward reducing the use of agrochemicals to achieve a more sustainable crop production. To this end, agricultural robots equipped with vision-based systems offer the potential to detect individual plants in the field automatically. This capability en
Robots and autonomous vehicles should be aware of what happens in their surroundings. The segmentation and tracking of moving objects are essential for reliable path planning, including collision avoidance. We investigate this estimation task for vehicles using radar sensing. We address moving insta…
Odometry and mapping play a pivotal role in the navigation of autonomous vehicles. In this paper, we address the problem of pose estimation and map creation using only radar sensors. We focus on two odometry estimation approaches followed by a mapping step. The first one is a new point-to-point ICP…
Localization is a crucial component for the navigation of autonomous vehicles. It encompasses global localization and place recognition, allowing a system to identify locations that have been mapped or visited before. Place recognition is commonly approached using cameras or LiDARs. However, these s
Many autonomous robotic applications require object-level understanding when deployed. Actively reconstructing objects of interest, i.e. objects with specific semantic meanings, is therefore relevant for a robot to perform downstream tasks in an initially unknown environment. In this work, we propos…
Computer vision techniques play a central role in the perception stack of autonomous vehicles. Such methods are employed to perceive the vehicle surroundings given sensor data. 3D LiDAR sensors are commonly used to collect sparse 3D point clouds from the scene. However compared to human perception s…
Semantic segmentation enables robots to perceive and reason about their environments beyond geometry. Most of such systems build upon deep learning approaches. As autonomous robots are commonly deployed in initially unknown environments, pre-training on static datasets cannot always capture the vari
Tracking changes in growing plants is important for automating phenotyping and robots managing crops. In this paper, we propose a system that uses a 3D model of plants along crop rows to enable a robotic platform to localize itself even in the presence of heavy changes and deforming the model to ada…
Forests play a crucial role in our ecosystems, functioning as carbon sinks, climate stabilizers, biodiversity hubs, and sources of wood. By the very nature of their scale, monitoring and maintaining forests is a challenging task. Robotics in forestry can have the potential for substantial automation…
Mobile robots that navigate in unknown environments need to be constantly aware of the dynamic objects in their surroundings for mapping, localization, and planning. It is key to reason about moving objects in the current observation and at the same time to also update the internal model of the stat
Object-based maps are relevant for scene under-standing since they integrate geometric and semantic information of the environment, allowing autonomous robots to robustly localize and interact with on objects. In this paper, we address the task of constructing a metric-semantic map for the purpose o…
A map of the environment is an essential component for robotic navigation. In the majority of cases, a map of the static part of the world is the basis for localization, planning, and navigation. However, dynamic objects that are presented in the scenes during mapping leave undesirable traces in the…
Our world is non-static, and robots should be able to track its changing geometry. For tracking changes, data asso-ciations between 3D points over time are key. In this paper, we investigate the problem of associating 3D points on plant organs from different mapping runs over time while the plants g…
Monitoring the traits of plants and fruits is a fundamental task in horticulture. With accurate measurements, farmers can predict the yield of their crops and use this information for making informed management decisions, and breeders can use it for variety selection. Agricultural robotic applicatio…
Scene understanding is crucial for autonomous robots in dynamic environments for making future state predictions, avoiding collisions, and path planning. Camera and LiDAR perception made tremendous progress in recent years, but face limitations under adverse weather conditions. To leverage the full
Plant phenotyping is a central task in agriculture, as it describes plants' growth stage, development, and other relevant quantities. Robots can help automate this process by accurately estimating plant traits such as the number of leaves, leaf area, and the plant size. In this paper, we address the…
Measuring plant traits with high throughput allows breeders to monitor and select the best cultivars for subsequent breeding generations. This can enable farmers to improve yield to produce more food, feed, and fiber. Current breeding practices involve extracting leaf parameters on a small subset of
Determining the state of a mobile robot is an essential building block of robot navigation systems. In this letter, we address the problem of estimating the robot's pose in an indoor environment using 2D LiDAR data and investigate how modern environment models can improve gold standard Monte-Carlo l
Robust and accurate pose estimation of a robotic platform, so-called sensor-based odometry, is an essential part of many robotic applications. While many sensor odometry systems made progress by adding more complexity to the ego-motion estimation process, we move in the opposite direction. By removi
Place recognition plays an important role in robot localization and SLAM. Being able to retrieve the current position in a given map allows, for instance, localizing without relying on GPS reception. In this letter, we address the problem of point cloud-based place recognition, we especially focus o
A distinctive representation of image patches in form of features is a key component of many computer vision and robotics tasks, such as image matching, image retrieval, and visual localization. State-of-the-art descriptors, from hand-crafted descriptors such as SIFT to learned ones such as HardNet,…
Mapping an environment is essential for several robotic tasks, particularly for localization. In this letter, we address the problem of mapping the environment using LiDAR point clouds with the goal to obtain a map representation that is well suited for robot localization. To this end, we utilize a
Lifelong localization in a given map is an essential capability for autonomous service robots. In this letter, we consider the task of long-term localization in a changing indoor environment given sparse CAD floor plans. The commonly used pre-built maps from the robot sensors may increase the cost a
Autonomous vehicles need to understand their surroundings geometrically and semantically to plan and act appropriately in the real world. Panoptic segmentation of LiDAR scans provides a description of the surroundings by unifying semantic and instance segmentation. It is usually solved in a bottom-u
Scene understanding is crucial for autonomous systems to reliably navigate in the real world. Panoptic segmentation of 3D LiDAR scans allows us to semantically describe a vehicle's environment by predicting semantic classes for each 3D point and to identify individual instances through different ins
Agricultural robots have the prospect to enable more efficient and sustainable agricultural production of food, feed, and fiber. Perception of crops and weeds is a central component of agricultural robots that aim to monitor fields and assess the plants as well as their growth stage in an automatic…
Monitoring plants and fruits at high resolution play a key role in the future of agriculture. Accurate 3D information can pave the way to a diverse number of robotic applications in agriculture ranging from autonomous harvesting to precise yield estimation. Obtaining such 3D information is non-trivi…
The awareness about moving objects in the surroundings of a self-driving vehicle is essential for safe and reliable autonomous navigation. The interpretation of LiDAR and camera data achieves exceptional results but typically requires to accumulate and process temporal sequences of data in order to…
Perception is crucial for robots that act in real-world environments, as autonomous systems need to see and understand the world around them to act properly. Panoptic segmentation provides an interpretation of the scene by computing a pixelwise semantic label together with instance IDs. In this pape…
Accurate mapping of large-scale environments is an essential building block of most outdoor autonomous systems. Challenges of traditional mapping methods include the balance between memory consumption and mapping accuracy. This paper addresses the problem of achieving large-scale 3D reconstruction u…
We investigate the task of object goal navigation in unknown environments where a target object is given as a semantic label (e.g. find a couch). This task is challenging as it requires the robot to consider the semantic context in diverse settings (e.g. TVs are often nearby couches). Most of the pr…
Crop inspection is a critical part of modern agricultural practices that helps farmers assess the current status of a field and then make crop management decisions. Current crop inspection methods are labour-intensive tasks, which makes them rather slow and expensive to apply. In this paper, we expl…
Semantic perception is a core building block in autonomous driving, since it provides information about the drivable space and location of other traffic participants. For learning-based perception, often a large amount of diverse training data is necessary to achieve high performance. Data labeling…
Precision farming robots offer the potential to reduce the amount of used agrochemicals through targeted interventions and thus are a promising step towards sustainable agriculture. A prerequisite for such systems is a robust plant classification system that can identify crops and weeds in various a
Agricultural robots have the potential to improve the efficiency and sustainability of existing agricultural practices. Most autonomous agricultural robots rely on machine vision systems. Such systems, however, often perform worse in new fields or when the robotic platforms change. While we can alle
Crops for food, feed, fiber, and fuel are key natural resources for our society. Monitoring plants and measuring their traits is an important task in agriculture often referred to as plant phenotyping. Traditionally, this task is done manually, which is time- and labor-intensive. Robots can automate
Understanding the scene is key for autonomously navigating vehicles, and the ability to segment the surroundings online into moving and non-moving objects is a central ingredient of this task. Often, deep learning-based methods are used to perform moving object segmentation (MOS). The performance of
Monitoring plants and fruits is important in modern agriculture, with applications ranging from high-throughput phenotyping to autonomous harvesting. Obtaining highly accurate 3D measurements under real agricultural conditions is a challenging task. In this letter, we address the problem of estimati
Scene understanding is critical for autonomous navigation in dynamic environments. Perception tasks in this domain like segmentation and tracking are usually tackled individually. In this letter, we address the problem of 4D panoptic segmentation using LiDAR scans, which requires to assign to each 3
Reliable and accurate registration of point clouds is a challenging problem in robotics as well as in the domain of autonomous driving. In this article, we address the task of aligning point clouds with low overlap, containing moving objects, and without prior information about the initial guess. We
Ego-motion estimation is a fundamental building block of any autonomous system that needs to navigate in an environment. In large-scale outdoor scenes, 3D LiDARs are often used for this task, as they provide a large number of range measurements at high precision. In this paper, we propose a novel ap
Robots that are supposed to interact with or manipulate objects in the world must be able to track the poses of objects in their sensor data. Thus, Detecting and tracking the 6-DoF poses of targeted objects is important for aerial manipulation and is still in the early stage due to the high dynamics…
Semantic segmentation of aerial imagery is an important tool for mapping and earth observation. However, supervised deep learning models for segmentation rely on large amounts of high-quality labelled data, which is labour-intensive and time-consuming to generate. To address this, we propose a new a…
Monitoring of fields and breeding plots is critical for farmers, plant scientists, and breeders. In this process, a key objective is to assess and monitor the growth stages together with the number of individual plants on the field. Traditionally, this in-field assessment is performed manually and t
In multi-agent settings, game theory is a natural framework for describing the strategic interactions of agents whose objectives depend upon one another's behavior. Trajectory games capture these complex effects by design. In competitive settings, this makes them a more faithful interaction model th…
Simultaneous Localization and Mapping (SLAM) systems are fundamental building blocks for any autonomous robot navigating in unknown environments. The SLAM implementation heavily depends on the sensor modality employed on the mobile platform. For this reason, assumptions on the scene's structure are…
Mapping systems that turn sensor data into a model of the environment are standard components in mobile robotics. Outdoor robots are often equipped with 3D LiDAR sensors to obtain accurate range measurements at a high frame rate. The price for a robotic LiDAR sensor scales roughly linearly with the
Real-time semantic segmentation of LiDAR data is crucial for autonomously driving vehicles and robots, which are usually equipped with an embedded platform and have limited computational resources. Approaches that operate directly on the point cloud use complex spatial aggregation operations, which
Monitoring individual plants and computing precise 3D reconstructions is highly relevant for crop breeding. In the conventional breeding approach, humans measure phenotypic traits by hand, requiring substantial manual labor. This paper addresses precise 3D plant reconstructions in a crop field or br…
A key challenge for autonomous vehicles is to navigate in unseen dynamic environments. Separating moving objects from static ones is essential for navigation, pose estimation, and understanding how other traffic participants are likely to move in the near future. In this work, we tackle the problem
Most autonomous driving and robotic applications require retrieving map data around the vehicle's current location. Those maps can cover large areas and are often stored in a compressed form to save memory and allow for efficient transmission. In this paper, we address the problem of place recogniti…
Robust localization in a given map is a crucial component of most autonomous robots. In this paper, we address the problem of localizing in an indoor environment that changes and where prominent structures have no correspondence in the map built at a different point in time. To overcome the discrepa…
Semantic scene interpretation is essential for autonomous systems to operate in complex scenarios. While deep learning-based methods excel at this task, they rely on vast amounts of labeled data that is tedious to generate and might not cover all relevant classes sufficiently. Self-supervised repres
Fine-grained scene understanding is essential for autonomous driving. The context around a vehicle can change drastically while navigating, making it hard to identify and understand the different objects that may appear. Although recent efforts on semantic and panoptic segmentation pushed the field
Creating accurate maps of complex, unknown environments is of utmost importance for truly autonomous navigation robot. However, building these maps online is far from trivial, especially when dealing with large amounts of raw sensor readings on a computation and energy constrained mobile system, suc…
Temporal semantic scene understanding is critical for self-driving cars or robots operating in dynamic environments. In this paper, we propose 4D panoptic LiDAR segmentation to assign a semantic class and a temporally-consistent instance ID to a sequence of 3D points. To this end, we present an appr…
Panoptic segmentation is the recently introduced task that tackles semantic segmentation and instance segmentation jointly [18]. In this paper, we present an extension of SemanticKITTI [1], a large-scale dataset providing dense point-wise semantic labels for all sequences of the KITTI Odometry Bench…
State estimation is a key ingredient in most robotic systems. Often, state estimation is performed using some form of least squares minimization. Basically, all error minimization procedures that work on real-world data use robust kernels as the standard way for dealing with outliers in the data. Th
Many modern robotics applications rely on 3D maps of the environment. Due to the large memory requirements of dense 3D maps, compression techniques are often necessary to store or transmit 3D maps efficiently. In this work, we investigate the problem of compressing dense 3D point cloud maps such as
The ability to localise is key for robot navigation. We describe an efficient method for vision-based localisation, which combines sequential Monte Carlo tracking with matching ground-level images to 2-D cartographic maps such as OpenStreetMaps. The matching is based on a learned embedded space repr…
Knowing the distance to nearby objects is crucial for autonomous cars to navigate safely in everyday traffic. In this paper, we investigate monocular depth estimation, which advanced substantially within the last years and is providing increasingly more accurate results while only requiring a single…
Robots and autonomous systems must interact with one another and their environment to provide high-quality services to their users. Dynamic game theory provides an expressive theoretical framework for modeling scenarios involving multiple agents with differing objectives interacting over time. A c…
Precision management of agricultural fields as well as plant breeding are central factors for keeping yields high and to provide food, feed, and fiber for our society. A key element in breeding trials but also for targeted management actions is to analyze the growth state of individual plants object
The registration of point clouds is a key ingredient of LiDAR-based SLAM systems and mapping approaches. A challenging task in this context is finding the right data association between 3D points. This paper proposes a novel and flexible graph network architecture to tackle the keypoint matching pro
The ability to predict the future movements of other vehicles is a subconscious and effortless skill for humans and key to safe autonomous driving. Therefore, trajectory prediction for autonomous cars has gained a lot of attention in recent years. It is, however, still a hard task to achieve human-l…
The ability to detect and segment moving objects in a scene is essential for building consistent maps, making future state predictions, avoiding collisions, and planning. In this letter, we address the problem of moving object segmentation from 3D LiDAR scans. We propose a novel approach that pushes
Accurately localizing in and mapping an environment are essential building blocks of most autonomous systems. In this paper, we present a novel approach for LiDAR odometry and mapping, focusing on improving the mapping quality and at the same time estimating the pose of the vehicle. Our approach per…
Robust and accurate, map-based localization is crucial for autonomous mobile systems. In this paper, we exploit range images generated from 3D LiDAR scans to address the problem of localizing mobile robots or autonomous cars in a map of a large-scale outdoor environment represented by a triangular m…
Exploiting past 3D LiDAR scans to predict future point clouds is a promising method for autonomous mobile systems to realize foresighted state estimation, collision avoidance, and planning. In this paper, we address the problem of predicting future 3D LiDAR point clouds given a sequence of past LiDA…
Robust and reliable ego-motion is a key component of most autonomous mobile systems. Many odometry estimation methods have been developed using different sensors such as cameras or LiDARs. In this work, we present a resilient approach that exploits the redundancy of multiple odometry algorithms usin…
In modern agriculture, measuring phenotypic traits helps breeders monitor plant growth, increase yield, and provide food, feed, and fiber. Traditional phenotyping requires intensive manual work, partially being intrusive. In this paper, we investigate the challenge of measuring phenotypic traits in…
Robots and autonomous systems need to know where they are within a map to navigate effectively. Thus, simultaneous localization and mapping or SLAM is a common building block of robot navigation systems. When building a map via a SLAM system, robots need to re-recognize places to find loop closure a…
Pose estimation and map building are central ingredients of autonomous robots and typically rely on the registration of sensor data. In this paper, we investigate a new metric for registering images that builds upon on the idea of the photometric error. Our approach combines a gradient orientation-b…
Inferring semantic information towards an understanding of the surrounding environment is crucial for autonomous vehicles to drive safely. Deep learning-based segmentation methods can infer semantic information directly from laser range data, even in the absence of other sensor modalities such as ca…
Annotated datasets are essential for supervised learning. However, annotating large datasets is a tedious and time-intensive task. This paper addresses active learning in the context of semantic segmentation with the goal of reducing the human labeling effort. Our application is agricultural robotic…
Localization is a crucial capability for mobile robots and autonomous cars. In this paper, we address learning an observation model for Monte-Carlo localization using 3D LiDAR data. We propose a novel, neural network-based observation model that computes the expected overlap of two 3D LiDAR scans. T…
Truly autonomous driving without the need for human intervention can only be attained when self-driving cars fully understand their surroundings. Most of these vehicles rely on a suite of active and passive sensors. LiDAR sensors are a cornerstone in most of these hardware stacks, and leveraging the…
Nowadays, mobile robots are deployed in many indoor environments such as offices or hospitals. These environments are subject to changes in the traversability that often happen following patterns. In this paper, we investigate the problem of navigating in such environments over extended periods of t…
Simultaneous localization and mapping (SLAM) is a fundamental capability required by most autonomous systems. In this paper, we address the problem of loop closing for SLAM based on 3D laser scans recorded by autonomous cars. Our approach utilizes a deep neural network exploiting different cues gene…
Plant phenotyping, i.e., the task of measuring plant traits to describe the anatomy and physiology of plants, is a central task in crop science and plant breeding. Standard methods often require intrusive or time-consuming operations involving a lot of manual labor. Cameras or range sensors, paired…
Analyzing sensor data of plants and monitoring plant performance is a central element in different agricultural robotics applications. In plant science, phenotyping refers to analyzing plant traits for monitoring growth, for describing plant properties, or characterizing the plant's overall performa…
Crops are an important source of food and other products. In conventional farming, tractors apply large amounts of agrochemicals uniformly across fields for weed control and plant protection. Autonomous farming robots have the potential to provide environment-friendly weed control on a per plant bas…
Autonomous navigation is a pre-requisite for field robots to carry out precision agriculture tasks. Typically, a robot has to navigate along a crop field multiple times during a season for monitoring the plants, for applying agrochemicals, or for performing targeted interventions. In this paper, we…
In this paper, we address the problem of combining 3D laser scanner and camera information to estimate the motion of a mobile platform. We propose a direct laser-visual odometry approach building upon photometric image alignment. Our approach is designed to maximize the information usage of both, th…
Robot navigation in outdoor environments is exposed to detrimental factors such as vibrations or power consumption due to the different terrains on which the robot navigates. In this paper, we address the problem of actively improving navigation by planning paths that aim at reducing over time pheno…
The ability to interpret a scene is an important capability for a robot that is supposed to interact with its environment. The knowledge of what is in front of the robot is, for example, relevant for navigation, manipulation, or planning. Semantic segmentation labels each pixel of an image with a cl…
For safety reasons, robotic lawn mowers and similar devices are required to stay within a predefined working area. Keeping the robot within its workspace is typically achieved by special safeguards such as a wire installed in the ground. In the case of robotic lawn mowers, this causes a certain cust…
Visual place recognition is a challenging task, especially in outdoor environments as the scenes naturally change their appearance. In this letter, we propose a method for visual place recognition that is able to deal with seasonal changes, different weather condition as well as illumination changes
Semantic scene understanding is important for autonomous robots that aim to navigate dynamic environments, manipulate objects, or interact with humans in a natural way. In this paper, we address the problem of jointly performing semantic segmentation as well as instance segmentation in an online fas…
Localizing a vehicle in a map is essential for automated driving and various other robotic applications. This paper addresses the problem of vehicle localization in urban environments. Our approach performs a graph-based sliding window optimization over a set of recent landmark and odometry measurem…
Perception in autonomous vehicles is often carried out through a suite of different sensing modalities. Given the massive amount of openly available labeled RGB data and the advent of high-quality deep learning algorithms for image-based recognition, high-level semantic perception tasks are pre-domi…
Mapping and localization are essential capabilities of robotic systems. Although the majority of mapping systems focus on static environments, the deployment in real-world situations requires them to handle dynamic objects. In this paper, we propose an approach for an RGB-D sensor that is able to co…
Localization is a pre-requisite for most autonomous robots. For example, to carry out precision agriculture tasks effectively, a robot must be able to localize itself accurately in crop fields. The crop field environment presents unique challenges such as the highly repetitive structure of the crops…
Semantic scene understanding is important for various applications. In particular, self-driving cars need a fine-grained understanding of the surfaces and objects in their vicinity. Light detection and ranging (LiDAR) provides precise geometric information about the environment and is thus a part of…
Reliable and accurate localization and mapping are key components of most autonomous systems. Besides geometric information about the mapped environment, the semantics plays an important role to enable intelligent navigation behaviors. In most realistic environments, this task is particularly compli…
Although most robots use probabilistic algorithms to solve state estimation problems, path planning is often performed without considering the uncertainty about the robot's position. Uncertainty, however, matters in planning, but considering it often leads to computationally expensive algorithms. In…
The ability to build maps is a key functionality for the majority of mobile robots. A central ingredient to most mapping systems is the registration or alignment of the recorded sensor data. In this paper, we present a general methodology for photometric registration that can deal with multiple diff…
Accurate and reliable localization and mapping is a fundamental building block for most autonomous robots. For this purpose, we propose a novel, dense approach to laser-based mapping that operates on three-dimensional point clouds obtained from rotating laser sensors. We construct a surfel-based map…
3D models of the environment are used in numerous robotic applications and should reflect the current state of the world. In this paper, we address the problem of quickly finding structural changes between the current state of the world and a given 3D model using a small number of images. Our approa…
Reducing the use of agrochemicals is an important component toward sustainable agriculture. Robots that can perform targeted weed control offer the potential to contribute to this goal, for example, through specialized weeding actions such as selective spraying or mechanical weed removal. A prerequi
Pose estimation and mapping are key capabilities of most autonomous vehicles and thus a number of localization and SLAM algorithms have been developed in the past. Autonomous robots and cars are typically equipped with multiple sensors. Often, the sensor suite includes a camera and a laser range fin…
Applying agrochemicals is the default procedure for conventional weed control in crop production, but has negative impacts on the environment. Robots have the potential to treat every plant in the field individually and thus can reduce the required use of such chemicals. To achieve that, robots need…
Extrinsic sensor calibration is an important task in robotics. There are various ways to perform the calibration task, but it often remains unclear which methods are better than the others. In this paper, we provide a systematic study about the calibration accuracy of three types of calibration meth…
Precision farming robots, which target to reduce the amount of herbicides that need to be brought out in the fields, must have the ability to identify crops and weeds in real time to trigger weeding actions. In this paper, we address the problem of CNN-based semantic segmentation of crop fields sepa…
Continuous crop monitoring is an important aspect of precision agriculture and requires the registration of sensor data over longer periods of time. Often, fields are monitored using cameras mounted on unmanned aerial vehicles (UAVs) but strong changes in the visual appearance of the growing crops a
3D laser scanners are frequently used sensors for mobile robots or autonomous cars and they are often used to perceive the static as well as dynamic aspects in the scene. In this context, matching 3D point clouds of objects is a crucial capability. Most matching methods such as numerous flavors of I…
Robotic lawn-mowers are required to stay within a predefined working area, otherwise they may drive into a pond or on the street. This turns navigation and path planning into safety critical components. If we consider using SLAM techniques in that context, we must be able to provide safety guarantee…
Most state estimation procedures in mobile robotics require information about the locations of the individual sensors on the platform. In this paper, we study the motion-based multi-sensor extrinsic calibration problem and point out an overlooked defect of traditional least squares estimation in thi…
Precision farming robots offer a great potential for reducing the amount of agro-chemicals that is required in the fields through a targeted, per-plant intervention. To achieve this, robots must be able to reliably distinguish crops from weeds on different fields and across growth stages. In this pa…
Unmanned aerial vehicles (UAVs) and other robots in smart farming applications offer the potential to monitor farm land on a per-plant basis, which in turn can reduce the amount of herbicides and pesticides that must be applied. A central information for the farmer as well as for autonomous agricult…
Robots for precision farming have the potential to reduce the reliance on herbicides and pesticides through selectively spraying individual plants or through manual weed removal. To achieve this, the value crops and the weeds must be identified by the robot's perception system to trigger the actuato
The demand for flexible industrial robotic solutions that are able to accomplish tasks at different locations in a factory is growing more and more. When deploying mobile robots in a factory environment, the predictability and reproducibility of their behaviors become important and are often request…
Maps are an important component of most robotic navigation systems and building maps under uncertainty is often referred to as simultaneous localization and mapping or SLAM. Most SLAM approaches start from scratch and build a map only based on their own observations and odometry information. In this…
Online pose estimation and mapping in unknown environments is essential for most mobile robots. Especially autonomous unmanned aerial vehicles require good pose estimates at comparably high frequencies. In this paper, we propose an effective system for online pose and simultaneous map estimation des
Object segmentation from 3D range data is an important topic in mobile robotics. A robot navigating in a dynamic environment needs to be aware of objects that might change or move. A segmentation of the laser scans into individual objects is typically the first processing step before a further analy…
Localization is an essential capability for mobile robots and the ability to localize in changing environments is key to robust outdoor navigation. Robots operating over extended periods of time should be able to handle substantial appearance changes such as those occurring over seasons or under dif
Fisheye cameras offer a large field of view, which is important for several robotics applications as a larger field of view allows for covering a large area with a single image. In contrast to classical cameras, however, fisheye cameras cannot be approximated well using the pinhole camera model and
Automated driving relies on fast, recent, accurate, and highly available pose estimates. A single localization system, however, can commonly ensure this only to some extent. In this paper, we propose a multi-sensor fusion approach that resolves this by combining multiple localization systems in a pl…
In autonomous exploration tasks, robots usually rely on a SLAM system to build a map of the environment online and then use it for navigation purposes. Although there has been substantial progress in robustly building accurate maps, these systems cannot guarantee the consistency of the resulting env
The ability to autonomously learn a model of an environment is an important capability of a mobile robot. In this paper, we investigate the problem of exploring a scene given background information in form of a topo-metric graph of the environment. Our method is relevant for several real-world appli
The ability to localize a robot is an important capability and matching of observations under substantial changes is a prerequisite for robust long-term operation. This paper investigates the problem of efficiently coping with seasonal changes in image data. We present an extension of a recent appro…
The ability to explore an unknown environment is an important prerequisite for building truly autonomous robots. The central decision that a robot needs to make when exploring an unknown environment is to select the next view point(s) for gathering observations. In this paper, we consider the proble…
As service robots become more and more capable of performing useful tasks for us, there is a growing need to teach robots how we expect them to carry out these tasks. However, learning our preferences is a nontrivial problem, as many of them stem from a variety of factors including personal taste, c…
In this paper, we present an appearance-based visual SLAM approach that focuses on detecting loop closures across seasons. Given two image sequences, our method first extracts one descriptor per image for both sequences using a deep convolutional neural network. Then, we compute a similarity matrix…
Quickly finding a free parking spot that is close to a desired target location can be a difficult task. This holds for human drivers and autonomous cars alike. In this paper, we investigate the problem of predicting the occupancy of parking spaces and exploiting this information during route plannin…