Monte Carlo Localization of Mobile Robot with Modified SIFT
The Scale Invariant Feature Transform, SIFT, is invariant to image translation, scaling, rotation, and is partially invariant to illumination changes. But, the time of features extraction and matching is huge, and the number of features is much larger then that is needed. To reduce the number of features generated by SIFT as well as their extraction and matching time, a modified approach based sampling is proposed. Mean-Shift algorithm is used in this modified SIFT to search local extrema points actively in scale space to improve the efficiency. The modified SIFT is used in Monte Carlo localization of mobile robots with omnidirectional sensor, it is demonstrated that the features extracted by modified SIFT are uniformly distributed in space, the time of feature extraction and matching is reduced obviously, and the mobile robots can localize itself accurately with a lower number of features.
robot localization scale invariant feature transform mean-shift particle filter omnidirectional vision
WANG Yu-quan XIA Gui-hua ZHU Qi-dan ZHAO Guo-liang
College of Automation Harbin Engineering University, HEU Harbin, China College of Automation Harbin Engineering University, HEU Harbin,China
国际会议
张家界
英文
400-403
2009-04-11（万方平台首次上网日期，不代表论文的发表时间）