18 accepted papers
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
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
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
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
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…
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
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
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…
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
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…
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…
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…
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…
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…
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…
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