ASME 07 Notes

Simutaneous Localization and Mapping (SLAM)

This is a method of robotic exploration and navigation in unknown environments.

--Authors of Problem Statement:

(1) Smith and Cheeseman 1987, (2) Leonard et al. 1992b, (3) Thrun 2003, (4) Thrun et al. 2005.

Solutions

Kalman Filtering

(See also Russell and Norvig pp. 551 - 559)

For "landmark-based SLAM:"

"We will primarily be concerned with landmark-based SLAM, for which the earliest and most popular methods are based on the extended Kalman filter (EKF) (Smith et al. 1987, 1990; Moutarlier and Chatila 1989a, 1989b;Ayache and Faugeras 1989; Leonard and Durrant-Whyte 1992, Leonard et al. 1992). The EKF recursively estimates a Gaussian density over the current pose of the robot and the position of all landmarks (the map). However, it is well known that the computational complexity of the EKF becomes intractable fairly quickly, and hence much effort has been focused on modifying and extending the filtering approach to cope with larger-scale environments (Newman 1999; Deans and Hebert 2000; Dissanayake et al. 2001; Castellanos et al. 1999; Julier and Uhlman 2001b; Leonard and Feder 2001; Guivant and Nebot 2001; Paskin 2003; Leonard and Newman 2003; Bosse et al. 2003; Thrun et al. 2004; Guivant et al 2004; Tardos et al. 2002; Williams et al. 2002; Rodriguez-Losada et al. 2004). However, filtering itself has been shown to be inconsistent when applied to the inherently non-linear SLAM problem (Julier and Uhlman 2001a), i.e., even the average taken over a large number of experiments diverges from the true solution. Since this is mainly due to linearization choices that cannot be undone in a filtering framework, there has recently been considerable interest in the smoothing version of the SLAM problem."

Tools used for computation

MATLAB for mathematical computation of the algorithms. 2-D world with dot landmarks and simulated noise.