多传感器融合的移动机器人室外激光SLAM算法优化与系统实现

Optimization and system implement of outdoor lidar SLAM algorithm for mobile robots based on multi-sensor fusion

针对移动机器人室外环境开阔场景大范围建图时,激光雷达里程计位姿计算不准确从而导致SLAM算法精度下降等问题,设计了一种基于多传感器融合的SLAM优化算法。算法上,通过前端里程计优化提升SLAM算法的可靠性,将适用于室外的GNSS等传感器信息与激光里程计融合,在技术上实现了扩展卡尔曼滤波的轻量化并将其嵌入于LOAM算法架构中,在尽可能不增加资源负担的情况下对前端里程计进行改进;在优化算法基础上,搭建了实际移动机器人平台并移植算法,实现了可供参考的多传感器融合硬件方案与扩展卡尔曼滤波在实际工程中处理多传感器数据的方法。真实场景下的实验结果表明,在增加了里程计运算量后算法仍能稳定保持10 Hz的室外建图,在复杂开阔环境与低成本条件下具有可靠性与可行性。

Aiming at the problem that the inaccurate pose calculation of the lidar odometry when mobile robots build the map in the outdoor open environment,which will make the accuracy of the simultaneous SLAM algorithm drops,an optimized SLAM algorithm based on multi-sensor fusion is designed.In terms of algorithm,the reliability of the SLAM algorithm is improved by optimizing the front-end odometry,the data of the lidar odometry is integrated with the data of several sensors which are suitable for outdoor use,such as GNSS,we achieve the lightweight of the extended Kalman filter and embed it in the LOAM algorithm technically,and improve the lidar odometry without increasing computing resource as much as possible.Based on the optimization algorithm,an actual mobile robot platform is built and the algorithm has been transplanted on it,the hardware solution of multi-sensor fusion and the method of processing extended Kalman filter in practical engineering are realized.The experimental results in real scenes show that the algorithm can be stably maintained at 10 Hz outdoor mapping after increasing the odometry calculation,and it is reliable and feasible in complex open environment and lowcost conditions in real scenes.