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Mobile Robot Monte Carlo Localization Based on Improved Local Invariant Feature Extraction Algorithm

LIU Dongbo, Hao Zhongxiao, XIAO Peng, LIU Changsong


To deal with the localization problem of robot equipped with monocular camera, a novel Monte Carlo method based on improved Scale Invariant Feature Transform (SIFT) is proposed. Firstly, the steps of constructing scale space, detecting extrema and confirming keypoints are employed to create the key feature points. Secondly, each keypoint is described by the rotation invariant Texture patterns, which are computed from the image patch centered at the keypoint, and be combined to SIFT feature to construct the union feature. Finally, a rotation invariant textured SIFT descriptor is formed. During robot motion, the information from feature observations is fused with that from the odometry by particle filter, which effectively improves the speed and accuracy of the localization. Experimental results demonstrate the validity of the approach.


Mobile Robot; SIFT Descriptors; Image Matching; Monte Carlo Localization

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