no code implementations • 10 Aug 2023 • D. Adriana Gómez-Rosal, Max Bergau, Georg K. J. Fischer, Andreas Wachaja, Johannes Gräter, Matthias Odenweller, Uwe Piechottka, Fabian Hoeflinger, Nikhil Gosala, Niklas Wetzel, Daniel Büscher, Abhinav Valada, Wolfram Burgard
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.
1 code implementation • 10 Oct 2022 • Kshitij Sirohi, Sajad Marvi, Daniel Büscher, Wolfram Burgard
Current learning-based methods typically try to achieve maximum performance for this task, while neglecting a proper estimation of the associated uncertainties.
1 code implementation • 29 Jun 2022 • Kshitij Sirohi, Sajad Marvi, Daniel Büscher, Wolfram Burgard
In this work, we introduce the novel task of uncertainty-aware panoptic segmentation, which aims to predict per-pixel semantic and instance segmentations, together with per-pixel uncertainty estimates.
no code implementations • 20 Oct 2021 • Kürsat Petek, Kshitij Sirohi, Daniel Büscher, Wolfram Burgard
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.
no code implementations • 11 Jun 2021 • Shengchao Yan, Tim Welschehold, Daniel Büscher, Wolfram Burgard
Our reinforcement learning agent learns a policy for a centralized controller to let connected autonomous vehicles at unsignalized intersections give up their right of way and yield to other vehicles to optimize traffic flow.
no code implementations • 16 Feb 2021 • Kshitij Sirohi, Rohit Mohan, Daniel Büscher, Wolfram Burgard, Abhinav Valada
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.
no code implementations • 9 Mar 2020 • Shengchao Yan, Jingwei Zhang, Daniel Büscher, Wolfram Burgard
In this paper we present an approach to learning policies for signal controllers using deep reinforcement learning aiming for optimized traffic flow.
1 code implementation • 23 Oct 2019 • Alexander Schaefer, Daniel Büscher, Johan Vertens, Lukas Luft, Wolfram Burgard
Due to their ubiquity and long-term stability, pole-like objects are well suited to serve as landmarks for vehicle localization in urban environments.
1 code implementation • 23 Oct 2019 • Alexander Schaefer, Johan Vertens, Daniel Büscher, Wolfram Burgard
Whether it is object detection, model reconstruction, laser odometry, or point cloud registration: Plane extraction is a vital component of many robotic systems.