Zobrazeno 1 - 10
of 121
pro vyhledávání: '"Buescher , Daniel"'
Autor:
Fischer, Georg K. J., Bergau, Max, Gómez-Rosal, D. Adriana, Wachaja, Andreas, Gräter, Johannes, Odenweller, Matthias, Piechottka, Uwe, Hoeflinger, Fabian, Gosala, Nikhil, Wetzel, Niklas, Büscher, Daniel, Valada, Abhinav, Burgard, Wolfram
Automated and autonomous industrial inspection is a longstanding research field, driven by the necessity to enhance safety and efficiency within industrial settings. In addressing this need, we introduce an autonomously navigating robotic system desi
Externí odkaz:
http://arxiv.org/abs/2402.07691
The availability of a robust map-based localization system is essential for the operation of many autonomously navigating vehicles. Since uncertainty is an inevitable part of perception, it is beneficial for the robustness of the robot to consider it
Externí odkaz:
http://arxiv.org/abs/2402.05840
Autor:
Gómez-Rosal, D. Adriana, Bergau, Max, Fischer, Georg K. J., Wachaja, Andreas, Gräter, Johannes, Odenweller, Matthias, Piechottka, Uwe, Hoeflinger, Fabian, Gosala, Nikhil, Wetzel, Niklas, Büscher, Daniel, Valada, Abhinav, Burgard, Wolfram
In today's chemical plants, human field operators perform frequent integrity checks to guarantee high safety standards, and thus are possibly the first to encounter dangerous operating conditions. To alleviate their task, we present a system consisti
Externí odkaz:
http://arxiv.org/abs/2308.05612
Modern autonomous systems often rely on LiDAR scanners, in particular for autonomous driving scenarios. In this context, reliable scene understanding is indispensable. Current learning-based methods typically try to achieve maximum performance for th
Externí odkaz:
http://arxiv.org/abs/2210.04472
Reliable scene understanding is indispensable for modern autonomous systems. Current learning-based methods typically try to maximize their performance based on segmentation metrics that only consider the quality of the segmentation. However, for the
Externí odkaz:
http://arxiv.org/abs/2206.14554
Robust localization in dense urban scenarios using a low-cost sensor setup and sparse HD maps is highly relevant for the current advances in autonomous driving, but remains a challenging topic in research. We present a novel monocular localization ap
Externí odkaz:
http://arxiv.org/abs/2110.10563
The transition from today's mostly human-driven traffic to a purely automated one will be a gradual evolution, with the effect that we will likely experience mixed traffic in the near future. Connected and automated vehicles can benefit human-driven
Externí odkaz:
http://arxiv.org/abs/2106.06369
Publikováno v:
IEEE Transactions on Robotics (T-RO), 2021
Panoptic segmentation of point clouds is a crucial task that enables autonomous vehicles to comprehend their vicinity using their highly accurate and reliable LiDAR sensors. Existing top-down approaches tackle this problem by either combining indepen
Externí odkaz:
http://arxiv.org/abs/2102.08009
Traffic signal controllers play an essential role in today's traffic system. However, the majority of them currently is not sufficiently flexible or adaptive to generate optimal traffic schedules. In this paper we present an approach to learning poli
Externí odkaz:
http://arxiv.org/abs/2003.04046
Publikováno v:
International Conference on Robotics and Automation , Montreal, QC, Canada, 2019, pp. 72-78
Whether it is object detection, model reconstruction, laser odometry, or point cloud registration: Plane extraction is a vital component of many robotic systems. In this paper, we propose a strictly probabilistic method to detect finite planes in org
Externí odkaz:
http://arxiv.org/abs/1910.11146