Benjamin Suger, Bastian Steder, and Wolfram Burgard.
Terrain-Adaptive Obstacle Detection.
In Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2016.

Abstract

Reliable detection and avoidance of obstacles is a crucial prerequisite for autonomously navigating robots as both guarantee safety and mobility. To ensure safe mobility, the obstacle detection needs to run online, thereby taking limited resources of autonomous systems into account. At the same time, robust obstacle detection is highly important. Here, a too conservative approach might restrict the mobility of the robot, while a more reckless one might harm the robot or the environment it is operating in. In this paper, we present a terrain-adaptive approach to obstacle detection that relies on 3D-Lidar data and combines computationally cheap and fast geometric features, like step height and steepness, which are updated with the frequency of the lidar sensor, with semantic terrain information, which is updated with at lower frequency. We provide experiments in which we evaluate our approach on a real robot on an autonomous run over several kilometers containing different terrain types. The experiments demonstrate that our approach is suitable for autonomous systems that have to navigate reliable on different terrain types including concrete, dirt roads and grass.

BibTeX entry:

@inproceedings{suger16iros,
  author = {Suger, Benjamin and Steder, Bastian and Burgard, Wolfram},
  booktitle = {Proc.~of the IEEE/RSJ International Conference on Intelligent Robots and
     Systems (IROS)},
  year = {2016},
  title = {Terrain-Adaptive Obstacle Detection}
}