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.
Sparse pose adjustment
Author Kurt Konolige
Video ID : 447
This video shows an illustration of pose-graph SLAM optimization, as described in Chap. 46.3.3, Springer Handbook of Robotics, 2nd edn (2016), using sparse pose adjustment.
Reference: K. Konolige, G. Grisetti, R. Kümmerle, W. Burgard, B. Limketkai, R. Vincent: Sparse pose adjustment for 2-D mapping, IEEE/RSJ Int. Conf. Intel. Robot. Syst. (IROS), Taipei (2010).