Robust Real-Time Local Laser Scanner Registration with Uncertainty Estimation

Abstract : We present a fast, robust method for registering successive laser rangefinder scans. Correspondences between the current scan and previous scans are determined. Gaussian uncertain- ties of the correspondences are generated from the data, and are used to fuse the data together into a unified egomotion es- timate using a Kalman process. Robustness is increased by us- ing a RANSAC variant to avoid invalid point correspondences. The algorithm is very fast; computational and memory require- ments are O(nlogn) where n is the number of points in a scan. Additionally, a covariance suitable for use in SLAM and filter techniques is cogenerated with the egomotion estimate. Results in large indoor environments are presented.
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Communication dans un congrès
6th International Conference on Field and Service Robotics - FSR 2007, Jul 2007, Chamonix, France. Springer, 42, 2007, Springer Tracts in Advanced Robotics
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Justin Carlson, Charles Thorpe, David Duke. Robust Real-Time Local Laser Scanner Registration with Uncertainty Estimation. 6th International Conference on Field and Service Robotics - FSR 2007, Jul 2007, Chamonix, France. Springer, 42, 2007, Springer Tracts in Advanced Robotics. 〈inria-00195850〉

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