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Simultaneous Localization and Mapping with Sparse Extended Information Filters

Yufeng Liu

Carnegie Mellon University, Pittsburgh, PA, USA

Daphne Koller

Andrew Y. Ng

Stanford University, Stanford, CA, USA

Zoubin Ghahramani

Gatsby Computational Neuroscience Unit, University College London, UK

Hugh Durrant-Whyte

University of Sydney, Sydney, Australia

In this paper we describe a scalable algorithm for the simultaneous mapping and localization (SLAM) problem. SLAM is the problem of acquiring a map of a static environment with a mobile robot. The vast majority of SLAM algorithms are based on the extended Kalman filter (EKF). In this paper we advocate an algorithm that relies on the dual of the EKF, the extended information filter (EIF). We show that when represented in the information form, map posteriors are dominated by a small number of links that tie together nearby features in the map. This insight is developed into a sparse variant of the EIF, called the sparse extended information filter (SEIF). SEIFs represent maps by graphical networks of features that are locally interconnected, where links represent relative information between pairs of nearby features, as well as information about the robot’s pose relative to the map. We show that all essential update equations in SEIFs can be executed in constant time, irrespective of the size of the map. We also provide empirical results obtained for a benchmark data set collected in an outdoor environment, and using a multi-robot mapping simulation.

Key Words: mobile robotics • mapping • SLAM • filters • Kalman filters • information filters • multi-robot systems • robotic perception • robot learning

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