Simultaneous Localization and Mapping View Full Text


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Chapter Info

DATE

2016-07-27

AUTHORS

Cyrill Stachniss , John J. Leonard , Sebastian Thrun

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-Dthree-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. More... »

PAGES

1153-1176

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-32552-1_46

DOI

http://dx.doi.org/10.1007/978-3-319-32552-1_46

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1005252971


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