11 accepted papers
The potential for deploying autonomous systems can be significantly increased by improving the perception and interpretation of the environment. However, the development of deep learning-based techniques for autonomous systems in unstructured outdoor environments poses challenges due to limited data…
Stereo matching can be used to estimate dense but inaccurate depth information for each pixel of a camera image. A LiDAR can provide accurate but sparse depth measurements. The fusion of both can combine their advantages. We propose an efficient method for fusing stereo and LiDAR at the cost level o…
To navigate safely, it is essential for a robot to detect all kinds of moving objects that could possibly interfere with the own trajectory. For common object classes, like cars, regular pedestrians, and trucks, there are large scale datasets as well as corresponding machine learning techniques, whi…
For autonomous driving in urban environments it is usually assumed that the road is flat. To drive off-road, however, we need a more sophisticated model of the ground surface. While previous work is mapping the terrain along with static obstacles, we propose to separate the tasks and introduce a new…
Fiducial markers like AprilTags play an important role in robotics, e.g., for the calibration of cameras or the localization of robots. One of the most important properties of an algorithm for detecting such tags is its localization accuracy.In this paper, we present the results of an extensive comp…
Localization is a key capability for autonomous vehicles. High-Definition maps are a popular method to represent the environment and to enable precise localization. However, the creation is very demanding and it is not always guaranteed to receive accurate map information, especially for unstructure…
In this paper, we describe how collision checking for car-like robots can be sped up utilizing system knowledge. Their non-holonomic motion, while being a challenge for motion planning, is utilized here to place discs which are used as an approximation of the robot's shape in a predictive manner. Fo…
We present a novel approach to trajectory generation that enables an autonomous vehicle to accurately follow a lead vehicle tracked by on-board sensors. In contrast to other approaches, we ignore the structure of the environment (e.g., lane markings), focusing purely on following the path driven by…
In this paper, we propose a system for autonomous vehicle following without a line of sight. From monocular camera images, the leading vehicle extracts scene descriptors which it transmits to the following vehicle by means of vehicle-to-vehicle (V2V) communication. The follower is able to recognize…
In this paper, we present a motion planning algorithm for autonomous navigation in highly constrained urban environments. Since common approaches to on-road trajectory planning turned out to be unsuitable for this task, we instead extended an A*-based planner originally designed for navigation in un…
We present a new mapping and navigation system based on human-recognizable landmarks with highly compact representations. Road segments, intersections and salient structures such as houses and trees are detected using vision and LiDAR data. The landmarks are entered in a sparse metrictopological map…