Real-time six degree-of-freedom pose estimation with ground vehicles represents a relevant and well studied topic in robotics, due to its many applications, such as autonomous driving and 3D mapping. Although some systems exist already, they are either not accurate or they struggle in real-time setting. In this paper, we propose a fast, accurate and modular LiDAR SLAM system for both batch and online estimation. We first apply downsampling and outlier removal, to filter out noise and reduce the size of the input point clouds. Filtered clouds are then used for pose tracking and floor detection, to ground-optimize the estimated trajectory. The availability of a pre-tracker, working in parallel with the filtering process, allows to obtain pre-computed odometries, to be used as aids when performing tracking. Efficient loop closure and pose optimization, achieved through a g2o pose graph, are the last steps of the proposed SLAM pipeline. We compare the performance of our system with state-of-the-art point cloud based methods, LOAM, LeGO-LOAM, A-LOAM, LeGO-LOAM-BOR and HDL, and show that the proposed system achieves equal or better accuracy and can easily handle even cases without loops. The comparison is done evaluating the estimated trajectory displacement using the KITTI and RADIATE datasets.
翻译:地面飞行器的实时6度自由估计是机器人中一个相关和研究周密的专题,因为其应用很多,例如自主驾驶和3D绘图。虽然有些系统已经存在,但它们不是不准确,就是在实时环境下挣扎。在本文中,我们提议为批量和在线估算建立一个快速、准确和模块化的LiDAR SLAM系统。我们首先采用下层抽取和外层清除,以过滤噪音和缩小输入点云的大小。然后,过滤的云被用来进行跟踪和地面探测,使估计轨迹达到最佳地面。预跟踪器的可用性与过滤程序并行,可以获取预合成的食谱,在进行跟踪时用作辅助工具。高效环绕封闭和显示优化,通过 g2o 方位图实现,是拟议的SLAM管道的最后步骤。我们系统的业绩与基于最新点的云层探测方法、LOAM、LOAM、LOAM、LeGOAM、LOAM、LOAM-LOA-OA、LOA-OA-OA-ODM-ODM-ODTI系统的表现可以更精确地评估,从而更精确地评估,可以更精确地评估或更精确地评估,可以更精确地评估。