基于稀疏位姿优化的激光SLAM算法研究

Research on Laser SLAM Algorithm Based on Sparse Pose Optimization

基于图优化的同步定位与建图(SLAM)算法的后端优化部分一般采用直接非线性优化方法。但是直接非线性优化方法的计算时间与图大小的立方成比例增长,优化大型位姿图成为移动机器人一个比较大的瓶颈问题。因此在基于图优化的框架下,采用基于稀疏位姿优化的SLAM算法,通过直接线性稀疏矩阵求解方法来高效计算约束图的大型稀疏矩阵,并与生成树初始化方式进行配合处理和优化。同时在自主搭建的移动机器人平台上进行实验,并在室内不同环境下对基于稀疏位姿优化的SLAM算法与Gmapping、Hector进行对比分析。结果表明,所提算法不仅在建图精度上有着明显的优势,而且内存占用也更小。

The back-end optimization part of the simultaneous localization and mapping(SLAM) algorithm based on graph optimization generally uses a direct nonlinear optimization method. However, the calculation time of the direct nonlinear optimization method increases proportionally with the cube of the graph size, and optimizing large-scale pose graphs has become a crucial bottleneck for mobile robots. Therefore, under the framework of graph optimization, the SLAM algorithm based on sparse pose optimization is used in this work to efficiently calculate the large sparse matrix of the constraint graph through the direct linear sparse matrix solving method. Additionally, it is processed and optimized by using the spanning-tree initialization method. At the same time, experiments are performed on an autonomously built mobile robot platform and the SLAM algorithm based on sparse pose optimization is compared with Gmapping and Hector algorithms in different indoor environments. Results show that the proposed algorithm is superior in mapping accuracy and has a lower CPU load.