YONG CHEN
ABSTRACT
SLAM technology has been widely used in intelligent robots. In order to solve the problems of pose estimation error, poor anti-interference ability and high calculation cost in traditional SLAM method under complex indoor environment, this paper proposes an improved method based on multi-modal sensor fusion, and the work such as feature extraction, data association and down-sampling are concerned in the research. The improved distribution function method based on RBPF and ICP algorithm is presented and applied in simulated experiments. Compared with the traditional RBPF method, which has low robustness and poor performance under complex and dynamic environment, the position estimation accuracy has been improved 43.4% averagely, and the computation consumption has been reduced as 49.1% while the improved algorithm being operated.
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