Home/Invariant filtering for simultaneous localization and mapping

Invariant filtering for simultaneous localization and mapping

Matthew Deans and Martial Hebert
Carnegie Mellon University, IEEE International Conference on Robotics and Automation, pp. 1042-7, April, 2000

Download Publication (PDF)

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.


This paper presents an algorithm for simultaneous localization and map building for a mobile robot moving in an unknown environment. The robot can measure only the bearings to identifiable targets and its own relative motion. The approach is to recursively estimate features of the environment which are invariant to the robot pose in order to decouple the pose error from the map error. The highly nonlinear nature of this problem requires more explicit reasoning about the spatial relationships between landmarks and between the robot and landmarks than those used in previous methods.

BibTeX Reference
title = {Invariant filtering for simultaneous localization and mapping},
author = {Matthew Deans and Martial Hebert},
booktitle = {IEEE International Conference on Robotics and Automation},
keyword = {localization, mapping},
school = {Robotics Institute , Carnegie Mellon University},
month = {April},
year = {2000},
pages = {1042-7},
address = {Pittsburgh, PA},