Exactly Sparse Extended Information Filters for Feature-based SLAM

Recent research concerning the Gaussian canonical form for Simultaneous Localization and Mapping (SLAM) has given rise to a handful of algorithms that attempt to solve the SLAM scalability problem for arbitrarily large environments. One such estimator that has received due attention is the Sparse Extended Information Filter (SEIF) by Thrun et al., which is reported to be nearly constant time, irrespective of the size of the map. The key to the SEIF’s scalability is to prune weak links in what is a dense information (inverse covariance) matrix to achieve a sparse approximation that allows for efficient, scalable SLAM. We demonstrate that the SEIF sparsification strategy yields error estimates that are overconfident when expressed in the global reference frame, while empirical results show that relative map consistency is maintained. In this paper, we propose an alternative scalable estimator based in the information form that maintains sparsity while preserving consistency. The pa...

• Type: Journal
• Year: 2007
• Where: IJRR
• Authors: Matthew Walter, Ryan Eustice, John J. Leonard
• Keywords: Gaussian Canonical Form, IJRR 2007, SEIF, SLAM Scalability Problem