Summary
The research paper discusses a stereo vision based mapping algorithm for modelling hazards in an urban environment for the case of a mobile robot. It consists of generating a depth of disparity map from the stereo images; the depth range coordinates calculated are then passed into a 3D grid generation algorithm to analyze the environment. This 3D grid is then segmented into planes which are checked against the plane of the robot to see if it is accessible or not. With this information a 2D local safety map is created which is used for the navigation of the robot.
Objective
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Method
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An algorithm known as the 6-DOF SLAM is employed for providing the robot’s pose to the mapping algorithm. Essentially, this is used for creating a disparity map from the image pair returned by the stereo camera setup. While post-processing, the range readings that are significantly different from neighbouring range readings are removed due to their likelihood of being incorrect. This localization step essentially helps figure the robot’s pose in 3D space and returns a set of 3D range points in the globe coordinate frame.
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The next step involves updating a 3D model consisting of a 3D grid and 3D point cloud with the new 3D range point readings using an occupancy algorithm. Essentially, for each 3D range point, a ray is cast from the camera to the 3D point and voxels along the ray have their log odds probability (LOP) of occupancy updated. A voxel starts off with a LOP of zero and is either incremented or decremented based on the change in the surroundings of the robot. Each voxel’s list is ordered according to the distance of the camera from the point and the voxels that have a high probability of occupancy are identified in the 3D occupancy grid.
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In this step, the 3D model is segmented into traversable ground regions and then planes are fitted to points in accordance with the segments using the linear least squares algorithm.
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Result
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The error rate for the stereo safety map versus ground truth safety map was analyzed. The FN rates are very less, however, the FP rate is quite high which means that the robot might detect an object when there is none.
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Inference
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References
https://web.eecs.umich.edu/~kuipers/papers/Murarka-iros-09.pdf
A. Murarka, M. Sridharan, and B. Kuipers, “Detecting obstacles and drop-offs using stereo and motion cues for safe local motion,” in IROS, 2008.
N. Heckman, J. Lalonde, N. Vandapel, and M. Hebert, “Potential negative obstacle detection by occlusion labeling,” in IROS, 2007.
J.-S. Gutmann, M. Fukuchi, and M. Fujita, “3D perception and environment map generation for humanoid robot navigation,” Intl. Journal of Robotics Research, 2008.
A. Rankin, A. Huertas, and L. Matthies, “Evaluation of stereo vision obstacle detection algorithms for off-road autonomous navigation,” in 32nd AUVSI Symposium on Unmanned Systems, 2005.
A. Murarka, “Building safety maps using vision for safe local mobile robot navigation,” Ph.D. dissertation, The University of Texas at Austin, 2009.doi.org/10.3390%2Fs18082571