Color Reflectance Modeling Using a Polychromatic Laser Range Sensor
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Ladar-Based Discrimination of Grass from Obstacles for Autonomous Navigation
ISER '00 Experimental Robotics VII
A Generative Model of Terrain for Autonomous Navigation in Vegetation
International Journal of Robotics Research
Efficient vision-based navigation
Autonomous Robots
Application of multi-modal features for terrain classification on a mobile system
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
Robotics and Autonomous Systems
Tracking-based semi-supervised learning
International Journal of Robotics Research
Spreading algorithm for efficient vegetation detection in cluttered outdoor environments
Robotics and Autonomous Systems
Probabilistic terrain classification in unstructured environments
Robotics and Autonomous Systems
| Hi-index | 0.00 |
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 vibration-based 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.