Abstract
Aiming at the SLAM problem faced by humanoid robot path planning in unknown environment, firstly, based on the analysis of feature region location and information description in the region, a feature extraction algorithm based on LPP has been researched to reduce the computational complexity of SLAM and the sensitivity to wrong data association. Secondly, aiming at the problems of phase winding and phase singularity in visual phase matching, a phase matching algorithm based on adaptive Gabor filter has been researched. By extracting the phase difference and visual difference of image pairs, there is no need to explicitly calculate the local phase, so as to achieve the best matching between the ideal phase difference and the world measured phase difference. Using unscented transform (UT) and selective acquisition strategy, a UTFastSLAM algorithm based on Rao-Blackwellised has been proposed, which effectively reduces the particle dilution phenomenon of particle filter (PF) and significantly improves the calculation accuracy and robustness of FastSLAM algorithm. Finally, the HRDA experimental platform has been constructed, and the forward and reverse kinematics models of HRDA have been established to verify the effectiveness of the algorithm proposed.