Cyrill Stachniss, John J. Leonard and Sebastian Thrun
This chapter provides a comprehensive introduction in to the simultaneous localization and mapping problem, better known in its abbreviated form as SLAM. SLAM addresses the main perception problem of a robot navigating an unknown environment. While navigating the environment, the robot seeks to acquire a map thereof, and at the same time it wishes to localize itself using its map. The use of SLAM problems can be motivated in two different ways: one might be interested in detailed environment models, or one might seek to maintain an accurate sense of a mobile robot’s location. SLAM serves both of these purposes.
We review the three major paradigms from which many published methods for SLAM are derived: (1) the extended Kalman filter (EKF); (2) particle filtering; and (3) graph optimization. We also review recent work in three-dimensional (3-D) SLAM using visual and red green blue distance-sensors (RGB-D), and close with a discussion of open research problems in robotic mapping.
Large-scale SLAM using the Atlas framework
Author Michael Bosse
Video ID : 440
This video shows the operation of the Atlas framework for real-time, large-scale mapping using the MIT Killian Court data set. Atlas employed graphs of coordinate frames. Each vertex in the graph represents a local coordinate frame, and each edge represents the transformation between adjacent local coordinate frames. In each local coordinate frame, extended Kalman filter SLAM (Chap. 46.3.1, Springer Handbook of Robotics, 2nd edn 2016) is performed to make a map of the local environment and to estimate the current robot pose, along with the uncertainties of each. Each map's uncertainties were modelled with respect to its own local frame. Probabilities of entities in relation to arbitrary map-frames were generated by following a path formed by the edges between adjacent map-frames, using Dijkstra's shortest path algorithm. Loop-closing was achieved via an efficient map matching algorithm.
Reference: M. Bosse, P. M. Newman, J. Leonard, S. Teller: Simultaneous localization and map building in large-scale cyclic environments using the Atlas framework, Int. J. Robot. Res. 23(12), 1113-1139 (2004).