Simultaneous Localization and Mapping with Sparse Extended Information Filters

Abstract

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.

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

“…Thrun et al (Thrun et al, 2004) proposes a sparsification method which represents the probability density in information form. Components close to zero in the normalized information matrix are ignored, leading to a sparse representation which can be efficiently updated with little compromise in performance.…”

Simultaneous Localization and Mapping in Marine Environments

“…The sparse extended information filter (SEIF) by Thrun et al [73] uses the information form of the EKF in combination with a sparsification method. One of the downfalls of that approach was that it resulted in overconfident estimates.…”

Fast and accurate SLAM with Rao–Blackwellized particle filters

“…Thrun et al [8] proposed a SEIF method which is based on the inverse of the covariance matrix. In this way, measurements can be integrated efficiently.…”

“…In the literature, the mobile robot mapping problem is often referred to as the simultaneous localization and mapping (SLAM) problem [1,2,3,4,5,6,7,8]. In general, SLAM is a complex problem because for learning a map the robot requires a good pose estimate while at the same time a consistent map is needed to localize the robot.…”