Robust Real-Time Local Laser Scanner Registration with Uncertainty Estimation

International audienceWe 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

Robust real-time local laser scanner registration with uncertainty estimation

We present a fast, robust method for registering successive laser rangefinder scans. Correspondences between the current scan and previous scans are determined. Gaussian uncertainties of the correspondences are generated from the data, and are used to fuse the data together into a unified egomotion estimate using a Kalman process. Robustness is increased by using a RANSAC variant to avoid invalid point correspondences. The algorithm is very fast; computational and memory requirements 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.