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.
Fast iterative alignment of pose graphs
Author Edwin Olson
Video ID : 444
This video provides an illustration of graph-based SLAM, as described in Chap. 46.3.3, Springer Handbook of Robotics, 2nd edn (2016), using the MIT Killian Court data set.
Reference: E. Olson, J. Leonard, S. Teller: Fast iterative alignment of pose graphs with poor initial estimates, Proc. IEEE Int. Conf. Robot. Autom. (ICRA), Orlando (2006), pp. 2262 - 2269; doi: 10.1109/ROBOT.2006.1642040.