Li Fuxing, Liu Shenglan, Zhao Xuedong, Zhang Liyan
College of Mechanical & Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Sensors (Basel). 2021 Oct 29;21(21):7162. doi: 10.3390/s21217162.
This study presents a 2-D lidar odometry based on an ICP (iterative closest point) variant used in a simple and straightforward platform that achieves real-time and low-drift performance. With a designated multi-scale feature extraction procedure, the lidar cloud information can be utilized at multiple levels and the speed of data association can be accelerated according to the multi-scale data structure, thereby achieving robust feature extraction and fast scan-matching algorithms. First, on a large scale, the lidar point cloud data are classified according to the curvature into two parts: smooth collection and rough collection. Then, on a small scale, noise and unstable points in the smooth or rough collection are filtered, and edge points and corner points are extracted. Then, the proposed tangent-vector-pairs based on edge and corner points are applied to evaluate the rotation term, which is significant for producing a stable solution in motion estimation. We compare our performance with two excellent open-source SLAM algorithms, Cartographer and Hector SLAM, using collected and open-access datasets in structured indoor environments. The results indicate that our method can achieve better accuracy.
本研究提出了一种基于迭代最近点（ICP）变体的二维激光雷达里程计，该里程计应用于一个简单直接的平台，实现了实时和低漂移性能。通过指定的多尺度特征提取过程，可以在多个层次上利用激光雷达点云信息，并根据多尺度数据结构加速数据关联的速度，从而实现鲁棒的特征提取和快速的扫描匹配算法。首先，在大尺度上，根据曲率将激光雷达点云数据分为两部分：平滑点集和粗糙点集。然后，在小尺度上，对平滑或粗糙点集中的噪声和不稳定点进行滤波，并提取边缘点和角点。接着，基于边缘点和角点提出的切向量对用于评估旋转项，这对于在运动估计中产生稳定解具有重要意义。我们使用在结构化室内环境中收集的和公开可用的数据集，将我们的性能与两种优秀的开源同步定位与地图构建（SLAM）算法——Cartographer和Hector SLAM进行比较。结果表明，我们的方法能够实现更高的精度。
