基于环境信息熵的点线视觉里程计 自适应优化器设计.

Aiming at the problem of inefficient operation of the point-line visual odometry (PL-VO) in the simultaneous location and mapping technology when the environmental texture changes, this paper designed feature extraction adaptive optimizer based on environmental information entropy to improve the efficiency and robustness of the original PL-VO algorithm. The optimizer used image information entropy as the main influencing factor to determine the optimal extraction features of the odometry, and generated a strategy map with included feature extraction options. It could also perform predictive calculations on the unexplored area, and quickly matched it with the strategy map to obtain the best feature extraction strategy for the area. It tested the average processing time and mapping effect of the PL-VO with an optimizer (APL-VO) in the TUM dataset. Experimental results show that APL-VO has stronger robustness and mapping efficiency in a hybrid environment than the original algorithm. [ABSTRACT FROM AUTHOR]