基于特征热力图的移动机器人高效全局定位方法

High-Efficient Global Localization Method of Mobile Robots Based on Feature Heat Map

针对大尺度场景下传统的全局定位算法搜索空间大、定位效率低的问题,提出了一种基于特征热力图的移动机器人高效全局定位方法.首先,融合光线投射和点/边特征检测算法,提取了环境栅格地图中的点/边信息;并制定了考虑随机误差的鲁棒处理策略,构建了具有鲁棒特征信息的特征热力图.此外,利用相同的策略提取了真实激光雷达点云中的点/边特征信息,并将特征信息与特征热力图进行匹配,获得了初始搜索空间.最后,对最大似然估计定位法与蒙特卡洛定位法进行了改进,实现了在初始搜索空间下的高效全局定位.在仿真场景和大尺度真实车间中进行了验证测试,相较于传统最大似然估计定位方法,计算时间下降幅度平均达到90.37%,而与蒙特卡洛定位方法相比,成功率平均提升了5.92倍.结果表明所提方法极大地缩小了定位搜索空间,有效地提高了全局定位效率和精度.

To solve the large-search-space and low-efficiency problems of traditional global localization algorithms in the large-scale scene, a high-efficient global localization method is proposed for mobile robots on the basis of the feature heat map. Firstly, the point/line information of the environmental grid map is extracted by combining the ray casting and point/line feature detection algorithms. Meanwhile, a robust processing strategy considering random errors is formulated, to construct the feature heat map with robust feature information. In addition, the similar strategy is applied to extracting the point/line feature information from real LiDAR point cloud. Moreover, the extracted feature information is matched with the feature heat map to obtain the initial search space. Finally, the maximum likelihood estimation localization and Monte Carlo localization methods are improved, thus the high-efficient global localization in the initial search space is accomplished.Verification tests are carried out in simulation scenarios and real large-scale workshops. Compared with the traditional maximum likelihood estimation localization method, the computing time is reduced by 90.37% averagely, while the success rate is 5.92 times higher averagely than the Monte Carlo localization method. Results show that the proposed method greatly reduces the localization search space and effectively improves the speed and accuracy of global localization.