Abstract
In order to meet the requirements of high-precision indoor navigation, a fast point cloud registration method based on Voxel-SIFT (Scale-Invariant Feature Transform) is proposed to improve the registration efficiency of LiDAR (Light Detection and Ranging). Due to the influence of information distribution factors, the dynamic adaptive system cannot be realized in the traditional federated filter. In this case, a hybrid federated filter based on weighted least square method is designed. It is proposed that the main filter in the hybrid federated filter adopts the minimum variance criterion and the optimal estimate of the sub-filter are fused according to the optimal coefficient weighted algorithm. The information distribution factor can be dynamically updated in real time to obtain a global optimal estimate. Finally, the experimental results show that the method can significantly improve the indoor positioning accuracy of mobile robots. The average positioning error is 0.22 m, which is more accurate than the visual odometer method or the LiDAR odometer method alone.
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