基于环境评价的IMU与CSM融合定位算法

Environmental assessment-based fusing algorithm for IMU and CSM localization

提出了一种基于环境评价的惯性测量单元(IMU)与相关性扫描匹配(CSM)融合定位算法.通过IMU估计机器人的初始位姿,根据当前激光扫描数据构建环境评价函数,将环境评价函数的输出与环境差异度阈值进行比较,实时更新CSM定位结果的置信度,通过置信度动态选择机器人位姿更新策略,解决了CSM在环境差异度较小的情况下易发生误匹配的问题.实验结果表明:所提方法的平移绝对误差及旋转绝对误差分别是CSM定位方法的10.1%和9.4%,是IMU辅助CSM定位方法的85.1%和93.3%,且计算效率相对CSM定位方法和IMU辅助CSM定位方法分别提高了12.6%和33.4%,从而验证了该方法环境适应性更强,且精度和效率更高.

An inertial measurement unit(IMU) and correlative scan matching(CSM) fusion localization method was presented based on environmental assessment.First,the robot’s initial pose was estimated by the IMU.Secondly,the environmental evaluation function was constructed based on the current scan. The output of the function was compared with the environmental difference threshold to update the confidence of CSM localization, and the robot ’s pose update strategy was dynamically selected with the confidence,which could improve the matching accuracy of CSM in case of small environmental difference.Finally,the experiment shows that the proposed method outperforms CSM-based localization and IMU aided CSM localization in terms of the pose estimation and computational efficiency,while the absolute translational error and rotational error of proposed method are 10.1% and 9.4% of CSM-based localization,and 85.1% and 93.3% of IMU aided CSM localization,and the computational efficiency of proposed method is increased by 12.6% and 33.4% compared with the other two methods,respectively.Therefore,it can be concluded that the proposed method is more adaptable to the environment and more accurate and efficient.