HRegNet-LO:基于端到端深度神经网络的LiDAR里程计测量  

HRegNet-LO: LiDAR Odometry Measurement Based on End-to-End Deep Neural Network

作　　者：付永健 李宗春[1] 何华 王力[1] 李丛[1] Fu Yongjian;Li Zongchun;He Hua;Wang Li;Li Cong(Institute of Geospatial Information,PLA Strategic Support Force Information Engineering University,Zhengzhou 450001,Henan,China)

机构地区：[1]战略支援部队信息工程大学地理空间信息学院,河南郑州450001

出　　处：《光学学报》2023年第24期271-279,共9页Acta Optica Sinica

基　　金：国家自然科学基金(42071454)。

摘　　要：针对传统LiDAR里程计(LO)测量方法在处理无初值、长序列点云配准时存在精度低、稳定性差等问题,本文引入端到端点云配准网络(HRegNet),提出一种基于深度神经网络的LO测量方法——HRegNet-LO算法,以期实现更加准确、鲁棒的LO测量。所提算法由两个核心模块组成:前端计算和后端优化。在前端scan-to-scan配准中,主要是依据原始点云的3D坐标,采用HRegNet网络,计算出相邻两帧点云的初始转换矩阵,实现LO初始位姿计算;在后端scan-tomap配准中,主要是通过提取特征点构建特征地图,应用迭代最近邻点(ICP)算法,每间隔一定距离对初始位姿进行优化,以减小预估轨迹中的漂移。在Kitti odometry数据集上对所提算法的性能进行了评估,并与LOAM、F-LOAM等算法作对比分析。实验结果表明,所提算法相对旋转、平移误差分别在0.003°/m和1%左右,每帧位姿计算耗时约为100 ms,可以满足LO测量对于精度和实时性的要求。Objective Vehicle odometer plays an important role in unmanned driving systems,and its main task is to determine the position and pose of the vehicle.The calculation method falls into two categories of visual odometry(VO)and LiDAR odometry(LO).VO is limited by illumination conditions,while LO is unaffected by illumination changes through active laser beam emission,making LO more suitable for vehicle odometry measurement than VO.However,the traditional LO algorithms face the problems of low accuracy and weak robustness when they are employed to register the continuous LiDAR frame point clouds without initial value and long sequence.We introduce an end-to-end point cloud registration network HRegNet and propose an LO computation algorithm using deep neural networks HRegNet-LO. Such a LO computation strategy exhibits strong robustness, good accuracy, and high efficiency, and we hope that the proposed algorithm can be helpful to the LO measurement.Methods The HRegNet-LO algorithm includes two core modules of front-end calculation and back-end optimization.The calculation is shown in Fig. 1.In frond-end calculation, scan-to-scan registration is conducted to obtain the transformation of adjacent two LiDAR frame point clouds. Firstly, HRegNet is employed to calculate a rough transformation matrix between two adjacent LiDAR frames without initial value by adopting the original point clouds. Secondly, according to point curvature, some feature points belonging to different categories are extracted from the original point clouds. Thirdly, the rough transformation matrix is refined by an iterative closest point (ICP) algorithm using feature points. Fourthly, LO initial pose is obtained by the sequence registration of the transformation matrix between two adjacent LiDAR frames.In back-end optimization, scan-to-map registration is conducted to adjust the LO initial pose. Firstly, a feature map is constructed using the initial LO pose and feature points. Secondly, by finding the corresponding feature points from the current Li

关 键 词：HRegNet网络 LiDAR里程计 特征地图 迭代最近邻点 Kitti odometry数据集 

分 类 号：TP701[自动化与计算机技术—检测技术与自动化装置]