Abstract-This paper addresses the problem of vegetation detection from laser measurements. The ability to detect vegetation is important for robots operating outdoors, since it enables a robot to navigate more efficiently and safely in such environments. In this paper, we propose a novel approach for detecting low, grass-like vegetation using laser remission values. In our algorithm, the laser remission is modeled as a function of distance, incidence angle, and material. We classify surface terrain based on 3D scans of the surroundings of the robot. The model is learned in a self-supervised way using vibrationbased terrain classification. In all real world experiments we carried out, our approach yields a classification accuracy of over 99%. We furthermore illustrate how the learned classifier can improve the autonomous navigation capabilities of mobile robots.
In REF these intensity values are used to detect grass-like vegetation.
Improving robot navigation in structured outdoor environments by identifying vegetation from laser data
2009 IEEE/RSJ International Conference on Intelligent Robots and Systems
2009 IEEE/RSJ International Conference on Intelligent Robots and Systems
Computer Science