Terrain Classification from 3-D Point Clouds for Autonomous Mobility
This project investigates extracting information from 3-D point clouds generated by mobility sensors (such as scanning ladars) for unmanned ground vehicles. The basic approach involves 1) computing features in a local neighborhood centered at each data point, 2) classifying each point using the local features, and 3) grouping the points into extended structures by enforcing spatial continuity. The features are based on the local spatial distribution of the dataand the local classifiers use standard statistical classifiers such as SVMs.
The algorithms developed in this project have been demonstrated on input from several different sensors and they were integrated in a complete unmanned ground vehicle system. Typical classification tasks include:
Classification output (red = load bearing surface; green = vegetation; blue = linear structures)
|Object map generation||
Input 3-D point cloud
Ouput object map
Input 3-D point cloud
Detected wire (in blue)
|Detection of organized structures||
Scene and input 3-D point cloud
Detected structure (in blue)
Many aspects of the 3-D terrain classification problem are addressed
in other projects. In particular, the 3-D point clouds are rapidly
updated (100K points/sec or more) and the density of the point
distribution may vary drastically across the point cloud. For these
reasons, new tools were developed for efficient computation on dynamically-update point clouds, and for estimating the characteristic scale of 3- D point clouds for local feature computation. In addition, tools and features specifically designed for structured environments (e.g., urban areas) are also investigated.
Jean-Francois Lalonde, Nicolas Vandapel, Daniel Huber, Martial Hebert, Natural Terrain Classification using Three-Dimensional Ladar Data for Ground Robot Mobility. Journal of Field Robotics, Volume 23, Issue 10, 2006.
Nick Heckman, Jean-Francois
Lalonde, Nicolas Vandapel,
Daniel Huber, Martial Hebert, Potential Negative Obstacle Detection by Occlusion Labeling. In the Proceedings of the
IEEE/RSJ Int'l Conf. on Intelligent Robots and Systems (IROS) ,
J. Tuley, Nicolas Vandapel,
Martial Hebert, Analysis and Removal of Artificats in 3-D LADAR Data. In Proceedings IEEE International Conference on Robotics and Automation
Nicolas Vandapel and Martial Hebert, Finding Organized Structures in 3-D Ladar Data. In the Proceedings of the IEEE/RSJ Int'l Conf. on Intelligent Robots and Systems (IROS) , 2004.Nicolas Vandapel, Daniel Huber, A. Kapuria, Martial Hebert, Natural terrain classification using 3-d ladar data. In Proc. IEEE Int'l Conf. on Robotics and Automation (ICRA) , 2004.
This research is supported by:
- ARL, Collaborative Technology Alliance Program, Cooperative