1 code implementation • 6 May 2024 • Xingguang Zhong, Yue Pan, Cyrill Stachniss, Jens Behley
We propose a novel approach for building accurate maps of dynamic environments utilizing a sequence of LiDAR scans.
1 code implementation • 20 Mar 2024 • Lucas Nunes, Rodrigo Marcuzzi, Benedikt Mersch, Jens Behley, Cyrill Stachniss
Our experimental evaluation shows that our method can complete the scene given a single LiDAR scan as input, producing a scene with more details compared to state-of-the-art scene completion methods.
1 code implementation • 12 Mar 2024 • Matteo Sodano, Federico Magistri, Lucas Nunes, Jens Behley, Cyrill Stachniss
Interpreting camera data is key for autonomously acting systems, such as autonomous vehicles.
1 code implementation • 17 Jan 2024 • Yue Pan, Xingguang Zhong, Louis Wiesmann, Thorbjörn Posewsky, Jens Behley, Cyrill Stachniss
In this paper, we propose a SLAM system for building globally consistent maps, called PIN-SLAM, that is based on an elastic and compact point-based implicit neural map representation.
no code implementations • 16 Jan 2024 • Gianmarco Roggiolani, Federico Magistri, Tiziano Guadagnino, Jens Behley, Cyrill Stachniss
Monitoring plants and measuring their traits is an important task in agriculture often referred to as plant phenotyping.
no code implementations • 22 Dec 2023 • Elias Marks, Jonas Bömer, Federico Magistri, Anurag Sah, Jens Behley, Cyrill Stachniss
Agricultural production is facing severe challenges in the next decades induced by climate change and the need for sustainability, reducing its impact on the environment.
no code implementations • 28 Sep 2023 • Matthias Zeller, Vardeep S. Sandhu, Benedikt Mersch, Jens Behley, Michael Heidingsfeld, Cyrill Stachniss
In this paper, we address the problem of moving instance segmentation in radar point clouds to enhance scene interpretation for safety-critical tasks.
no code implementations • 7 Jun 2023 • Jan Weyler, Federico Magistri, Elias Marks, Yue Linn Chong, Matteo Sodano, Gianmarco Roggiolani, Nived Chebrolu, Cyrill Stachniss, Jens Behley
The production of food, feed, fiber, and fuel is a key task of agriculture.
no code implementations • 22 Mar 2023 • Gianmarco Roggiolani, Federico Magistri, Tiziano Guadagnino, Jan Weyler, Giorgio Grisetti, Cyrill Stachniss, Jens Behley
Furthermore, the pre-trained networks obtain similar performance to the fully supervised with less labeled data.
1 code implementation • 20 Mar 2023 • Nicky Zimmerman, Matteo Sodano, Elias Marks, Jens Behley, Cyrill Stachniss
We exploit 3D object detections from monocular RGB frames for both, the object-based map construction, and for globally localizing in the constructed map.
1 code implementation • 15 Mar 2023 • Yue Pan, Federico Magistri, Thomas Läbe, Elias Marks, Claus Smitt, Chris McCool, Jens Behley, Cyrill Stachniss
Monitoring plants and fruits at high resolution play a key role in the future of agriculture.
1 code implementation • CVPR 2023 • Lucas Nunes, Louis Wiesmann, Rodrigo Marcuzzi, Xieyuanli Chen, Jens Behley, Cyrill Stachniss
Especially in autonomous driving, point clouds are sparse, and objects appearance depends on its distance from the sensor, making it harder to acquire large amounts of labeled training data.
no code implementations • 7 Dec 2022 • Matthias Zeller, Jens Behley, Michael Heidingsfeld, Cyrill Stachniss
Scene understanding is crucial for autonomous robots in dynamic environments for making future state predictions, avoiding collisions, and path planning.
1 code implementation • 25 Nov 2022 • Hanna Müller, Nicky Zimmerman, Tommaso Polonelli, Michele Magno, Jens Behley, Cyrill Stachniss, Luca Benini
Experimental evaluation using a nano-UAV open platform demonstrated that the proposed solution is capable of localizing on a 31. 2m$\boldsymbol{^2}$ map with 0. 15m accuracy and an above 95% success rate.
1 code implementation • 14 Oct 2022 • Gianmarco Roggiolani, Matteo Sodano, Tiziano Guadagnino, Federico Magistri, Jens Behley, Cyrill Stachniss
In this paper, we address the problem of joint semantic, plant instance, and leaf instance segmentation of crop fields from RGB data.
1 code implementation • 6 Oct 2022 • Matteo Sodano, Federico Magistri, Tiziano Guadagnino, Jens Behley, Cyrill Stachniss
We propose a novel encoder-decoder neural network that processes RGB and depth separately through two encoders.
1 code implementation • 6 Oct 2022 • Haofei Kuang, Xieyuanli Chen, Tiziano Guadagnino, Nicky Zimmerman, Jens Behley, Cyrill Stachniss
The experiments suggest that the presented implicit representation is able to predict more accurate 2D LiDAR scans leading to an improved observation model for our particle filter-based localization.
1 code implementation • 5 Oct 2022 • Xingguang Zhong, Yue Pan, Jens Behley, Cyrill Stachniss
Accurate mapping of large-scale environments is an essential building block of most outdoor autonomous systems.
1 code implementation • 8 Jun 2022 • Benedikt Mersch, Xieyuanli Chen, Ignacio Vizzo, Lucas Nunes, Jens Behley, Cyrill Stachniss
A key challenge for autonomous vehicles is to navigate in unseen dynamic environments.
1 code implementation • 28 Sep 2021 • Benedikt Mersch, Xieyuanli Chen, Jens Behley, Cyrill Stachniss
In this paper, we address the problem of predicting future 3D LiDAR point clouds given a sequence of past LiDAR scans.
1 code implementation • CVPR 2021 • Mehmet Aygün, Aljoša Ošep, Mark Weber, Maxim Maximov, Cyrill Stachniss, Jens Behley, Laura Leal-Taixé
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.
no code implementations • 4 Mar 2020 • Jens Behley, Andres Milioto, Cyrill Stachniss
Panoptic segmentation is the recently introduced task that tackles semantic segmentation and instance segmentation jointly.
2 code implementations • IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2019 • Andres Milioto, Ignacio Vizzo, Jens Behley, Cyrill Stachniss
Perception in autonomous vehicles is often carried out through a suite of different sensing modalities.
Ranked #19 on Robust 3D Semantic Segmentation on SemanticKITTI-C
1 code implementation • 6 May 2019 • Emanuele Palazzolo, Jens Behley, Philipp Lottes, Philippe Giguère, Cyrill Stachniss
For localization and mapping, we employ an efficient direct tracking on the truncated signed distance function (TSDF) and leverage color information encoded in the TSDF to estimate the pose of the sensor.
Robotics
5 code implementations • ICCV 2019 • Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyrill Stachniss, Juergen Gall
Despite the relevance of semantic scene understanding for this application, there is a lack of a large dataset for this task which is based on an automotive LiDAR.
Ranked #32 on 3D Semantic Segmentation on SemanticKITTI
no code implementations • 9 Jun 2018 • Philipp Lottes, Jens Behley, Andres Milioto, Cyrill Stachniss
Exploiting the crop arrangement information that is observable from the image sequences enables our system to robustly estimate a pixel-wise labeling of the images into crop and weed, i. e., a semantic segmentation.
no code implementations • 9 Jun 2018 • Philipp Lottes, Jens Behley, Nived Chebrolu, Andres Milioto, Cyrill Stachniss
It outputs the stem location for weeds, which allows for mechanical treatments, and the covered area of the weed for selective spraying.