2014 | OriginalPaper | Buchkapitel
Accuracy Improvement of Localization and Mapping of ICP-SLAM via Competitive Associative Nets and Leave-One-Out Cross-Validation
verfasst von : Shuichi Kurogi, Yoichiro Yamashita, Hikaru Yoshikawa, Kotaro Hirayama
Erschienen in: Neural Information Processing
Verlag: Springer International Publishing
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This paper presents a method to improve the accuracy of localization and mapping obtained by ICP-SLAM (iterative closest point - simultaneous localization and mapping) algorithm. The method uses competitive associative net (CAN2) for learning piecewise linear approximation of the cloud of 2D points obtained by the LRF (laser range finder) mounted on a mobile robot. To reduce the propagation error caused by the consecutive pairwise registration by the ICP-SLAM algorithm, the present method utilizes leave-one-out cross-validation (LOOCV) and tries to minimize the LOOCV registration error. The effectiveness is shown by analyzing the real experimental data.