Simultaneous Mapping and Localization with Sparse Extended Information Filters: Theory and Initial Results (revised)
Abstract
This paper describes a scalable algorithm for the simultaneous localization and mapping (SLAM) problem. SLAM is the problem of determining the location of environmental features with a roving robot. Many of today's popular techniques are based on extended Kalman filters (EKFs), which require update time quadratic in the number of features in the map. This paper develops the notion of sparse extended information filters (SEIFs) as a new method for solving the SLAM problem. SEIFs exploit structure inherent in the SLAM problem, representing maps through local, Web-like networks of features. By doing so, updates can be performed in constant time, irrespective of the number of features in the map. This paper presents several original constant-time results of SEIFs, and provides simulation results that show the high accuracy of the resulting maps in comparison to the computationally more cumbersome EKF solution.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 20, 2002
- Accession Number
- ADA457608
Entities
People
- Andrew Y. Ng
- Daphne Koller
- Hugh Durrant-whyte
- Sebastian Thrun
- Zoubin Ghahramani
Organizations
- Carnegie Mellon University