12 accepted papers
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 in typical downstream tasks of navigation stacks.
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 safe operation of a system in the real world it i
Modern autonomous systems often rely on LiDAR scanners, in particular for autonomous driving scenarios. In this context, reliable scene understanding is indispensable. Conventional learning-based methods generally try to achieve maximum performance for this task, while neglecting a proper estimation…
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 ones and the whole traffic system in different way
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 approach based on a sliding-window pose graph that l…
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 learn policies for signal controllers using deep reinforcement…
Horizontally scanning 2D laser rangefinders are a popular approach for indoor robot localization because of the high accuracy of the sensors and the compactness of the required 2D maps. As the scanners in this configuration only provide information about one slice of the environment, the measurement…
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 organized 3-D laser range scans. An agglomerative hie…
The Bayes filter is the basis for many state-of-the-art robot localization algorithms. In the literature, its derivation typically requires the robot controls to be chosen independently of all other variables. However, this assumption is not valid for a robotic system that is to act purposefully. Th
Man-made environments such as households, offices, or factory floors are typically composed of linear structures. Accordingly, polylines are a natural way to accurately represent their geometry. In this paper, we propose a novel probabilistic method to extract polylines from raw 2-D laser range scan…
The ability to build models of the environment is an essential prerequisite for many robotic applications. In recent years, mapping of dense surface geometry using RGB-D cameras has seen extensive progress. Many approaches build colored models, typically directly using the intensity values provided…
Most of the conventional approaches to mobile robot navigation avoid any kind of contact with the environment or with humans. As nowadays distance sensors typically have a limited - and often only two-dimensional - field of view, collisions with the environment or contacts with humans cannot be full…