基于环境信息熵的点线视觉里程计自适应优化器设计

Design of PL-VO adaptive optimizer based on environmental information entropy

针对即时定位与建图技术中点线视觉里程计在环境纹理发生变化时运行效率低下的问题,设计了一种基于环境信息熵的特征提取自适应优化器,以提高原有点线视觉里程计算法的效率及鲁棒性。优化器以图像信息熵作为主要影响因子,确定里程计的最优提取特征,生成包含特征提取选择的策略信息地图;对未探索区域的纹理环境进行预判性计算,与策略地图快速匹配,得到该区域的最优特征提取策略。在TUM数据集环境下测试了具有优化器的点线视觉里程计(APL-VO)的平均处理时间及建图效果。实验结果显示,与原有算法相比,具有自适应优化器的点线视觉里程计在复合环境中具有更强的鲁棒性及建图效率。

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 tes-ted 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.