Graphics enhanced version of this site

Robust Real-Time Local Laser Scanner Registration with Uncertainty Estimation
J.D. Carlson, C. Thorpe, and D. Duke
International Conference on Field and Service Robotics, July, 2007.

Jump to: Download | Abstract | Text Reference | BibTeX Reference

Adobe portable document format (pdf) [488 KB]

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

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.

J.D. Carlson, C. Thorpe, and D. Duke, "Robust Real-Time Local Laser Scanner Registration with Uncertainty Estimation," International Conference on Field and Service Robotics, July, 2007.

@inproceedings{Carlson_2007_5936,
   author = "Justin David Carlson and Chuck Thorpe and David Duke",
   title = "Robust Real-Time Local Laser Scanner Registration with Uncertainty Estimation",
   booktitle = "International Conference on Field and Service Robotics",
   month = "July",
   year = "2007"
}