Hugh Durrant-Whyte and Tim Bailey

Simultaneous Localisation and Mapping (SLAM):
Part I The Essential Algorithms

Robotics and Automation Magazine, June, 2006


 

Description

This is the first of a two-part tutorial on SLAM. It describes the algorithm's history, essential form, and the two key Bayesian realisations: EKF-SLAM and FastSLAM. It also provides links to online SLAM software and data.


Abstract

This tutorial provides an introduction to Simultaneous Localisation and Mapping (SLAM) and the extensive research on SLAM that has been undertaken over the past decade. SLAM is the process by which a mobile robot can build a map of an environment and at the same time use this map to compute it's own location. The past decade has seen rapid and exciting progress in solving the SLAM problem together with many compelling implementations of SLAM methods. Part I of this tutorial (this paper), describes the probabilistic form of the SLAM problem, essential solution methods and significant implementations. Part II of this tutorial will be concerned with recent advances in computational methods and new formulations of the SLAM problem for large scale and complex environments.

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Full paper [pdf] (500 kb, 9 pages)


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