Terrain-adaptive obstacle detection

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.

Authors

• Benjamin Suger
• Bastian Steder
• Wolfram Burgard

Keywords

• Robot sensing systems
• Navigation
• Laser radar
• Roads
• Semantics
• Hardware design languages
• Obstacle Avoidance
• Autonomic System
• Semantic Information
• Step Height
• Terrain Types
• Dirt Roads
• Cell State
• Remission
• Random Forest
• Power Calculation
• Time Constant
• Urban Environments
• Mixture Model
• Expectation Maximization
• Random Forest Classifier
• Path Planning
• Pitch Angle
• Grid Map
• Naive Approach
• Geometric Measures
• Terrain Analysis
• Forest Road
• Geometrical Considerations
• Robot Capabilities
• Quad-core CPU