Xingzhe Xie and Heng Wang and Ran Liu and Wen-qiang Xiang and Ming Jiang

3D Terrain Reconstruction for Patrol Robot Using Point Grey Research Stereo Vision Cameras

Proceedings of the 2010 International Conference on Artificial Intelligence and Computational Intelligence (AICI '10), Sanya, China vol. 1, October, 2010, pp. 47-51


Abstract

This paper uses Bumblebee stereo vision system to reconstruct the 3D terrain for patrol robot. Firstly, with the selected points in the disparity image, the ground plane equation of the current frame is calculated by the RANSAC (Random Sample Consensus) algorithm, and then estimated and predicted by the Kalman filter. Secondly, the elevation image is obtained through the distance between the obstacle and the ground plane, which is calculated on basis of the formula from the point to the plane. Finally, the noise points in the elevation image are removed through erosion and dilation operations, and the location and size of obstacles are determined via the connected component analysis method. The actual test in substation environment verified the reliability of the system.


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BibTeX

@inproceedings{Liu2010:3D,
  author = {Xingzhe Xie and Heng Wang and Ran Liu and Wen-qiang Xiang and Ming
	Jiang},
  title = {3D Terrain Reconstruction for Patrol Robot Using Point Grey Research
	Stereo Vision Cameras},
  booktitle = {Proceedings of the 2010 International Conference on Artificial Intelligence
	and Computational Intelligence (AICI '10)},
  year = {2010},
  volume = {1},
  pages = {47--51},
  address = {Sanya, China},
  month = {October},
  abstract = {This paper uses Bumblebee stereo vision system to reconstruct the
	3D terrain for patrol robot. Firstly, with the selected points in
	the disparity image, the ground plane equation of the current frame
	is calculated by the RANSAC (Random Sample Consensus) algorithm,
	and then estimated and predicted by the Kalman filter. Secondly,
	the elevation image is obtained through the distance between the
	obstacle and the ground plane, which is calculated on basis of the
	formula from the point to the plane. Finally, the noise points in
	the elevation image are removed through erosion and dilation operations,
	and the location and size of obstacles are determined via the connected
	component analysis method. The actual test in substation environment
	verified the reliability of the system.},
  acmid = {1917716},
  days = {23-24},
  doi = {http://dx.doi.org/10.1109/AICI.2010.17},
  isbn = {978-0-7695-4225-6},
  keywords = {patrol robot, RANSAC algorithm, Kalman filter, connected component
	analysis},
  numpages = {5},
}