21 accepted papers
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
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
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…
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
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…
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
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
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
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
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
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…
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
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
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…
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 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