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Published in:
2006 | OriginalPaper | Chapter
Scan-SLAM: Combining EKF-SLAM and Scan Correlation
Authors : Juan Nieto, Tim Bailey, Eduardo Nebot
Published in: Field and Service Robotics
Publisher: Springer Berlin Heidelberg
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This paper presents a new generalisation of simultaneous localisation and mapping (SLAM). SLAM implementations based on
extended Kalman filter
(EKF) data fusion have traditionally relied on simple geometric models for defining landmarks. This limits EKF-SLAM to environments suited to such models and tends to discard much potentially useful data. The approach presented in this paper is a marriage of EKF-SLAM with scan correlation. Instead of geometric models, landmarks are defined by templates composed of raw sensed data, and scan correlation is shown to produce landmark observations compatible with the standard EKF-SLAM framework. The resulting
Scan
-SLAM combines the general applicability of scan correlation with the established advantages of an EKF implementation: recursive data fusion that produces a convergent map of landmarks and maintains an estimate of uncertainties and correlations. Experimental results are presented which validate the algorithm.