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Data Association of Robot Localization and Mapping Using Partial Compatibility Test
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 Title & Authors
Data Association of Robot Localization and Mapping Using Partial Compatibility Test
Yan, Rui Jun; Choi, Youn Sung; Wu, Jing; Han, Chang Soo;
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 Abstract
This paper presents a natural corners-based SLAM (Simultaneous Localization and Mapping) with a robust data association algorithm in a real unknown environment. Corners are extracted from raw laser sensor data, which are chosen as landmarks for correcting the pose of mobile robot and building the map. In the proposed data association method, the extracted corners in every step are separated into several groups with small numbers of corners. In each group, local best matching vector between new corners and stored ones is found by joint compatibility, while nearest feature for every new corner is checked by individual compatibility. All these groups with local best matching vector and nearest feature candidate of each new corner are combined by partial compatibility with linear matching time. Finally, SLAM experiment results in an indoor environment based on the extracted corners show good robustness and low computation complexity of the proposed algorithms in comparison with existing methods.
 Keywords
SLAM;Partial compatibility test;Data association;Partial compatibility branch and bound;Nearest unit-matching branch and bound;
 Language
Korean
 Cited by
 References
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