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Simultaneous localization and mapping
Stachniss, C.; Leonard, J.J.; Thrun, S. (2016). Simultaneous localization and mapping, in: Siciliano, B. et al. Springer handbook of robotics. pp. 1153-1176. https://dx.doi.org/10.1007/978-3-319-32552-1_46
In: Siciliano, B.; Khatib, O. (Ed.) (2016). Springer handbook of robotics. Second edition. Springer Verlag: Berlin. ISBN 978-3-319-32550-7; e-ISBN 978-3-319-32552-1. LXXVI, 2227 pp. https://dx.doi.org/10.1007/978-3-319-32552-1, meer
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  • Stachniss, C.
  • Leonard, J.J.
  • Thrun, S.
Abstract
    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.
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