Simultaneous Localization and Mapping (SLAM) has wide robotic applications such as autonomous driving and unmanned aerial vehicles. Both computational efficiency and localization accuracy are of great importance towards a good SLAM system. Existing works on LiDAR based SLAM often formulate the problem as two modules: scan-to-scan match and scan-to-map refinement. Both modules are solved by iterative calculation which are computationally expensive. In this paper, we propose a general solution that aims to provide a computationally efficient and accurate framework for LiDAR based SLAM. Specifically, we adopt a non-iterative two-stage distortion compensation method to reduce the computational cost. For each scan input, the edge and planar features are extracted and matched to a local edge map and a local plane map separately, where the local smoothness is also considered for iterative pose optimization. Thorough experiments are performed to evaluate its performance in challenging scenarios, including localization for a warehouse Automated Guided Vehicle (AGV) and a public dataset on autonomous driving. The proposed method achieves a competitive localization accuracy with a processing rate of more than 10 Hz in the public dataset evaluation, which provides a good trade-off between performance and computational cost for practical applications.
翻译:同时的本地化和绘图(SLAM)具有广泛的机器人应用,如自主驾驶飞行器和无人驾驶飞行器。计算效率和本地化精确度对于良好的SLAM系统都非常重要。基于LIDAR的SLAM的现有工程往往将问题分为两个模块:扫描到扫描匹配和扫描到映射完善。两个模块都是通过反复计算解决的,计算成本很高。在本文件中,我们提出了一个一般性解决方案,旨在为基于LIDAR的SLAM提供一种计算高效和准确的框架。具体地说,我们采用了一种不附带两阶段扭曲补偿方法来降低计算成本。对于每个扫描输入,均提取边缘和平面特征,并与当地边缘地图和本地平面地图分别匹配,其中也考虑对地方的光滑度进行迭接的表面优化。进行索罗式实验,以评价其在具有挑战性情景中的性能,包括仓储自动导航飞行器(AGV)的本地化和自主驱动的公开数据集。拟议方法在公共数据计算中实现竞争性本地化,处理率超过10赫兹,在公共数据配置的应用程序中提供良好的贸易性测试。