Localization and mapping algorithm based on Lidar-IMU-Camera fusion  

作　　者：Yibing Zhao Yuhe Liang Zhenqiang Ma Lie Guo Hexin Zhang 

机构地区：[1]School of Mechanical Engineering,Dalian University of Technology,Dalian 116024,China [2]Department of the Built Environment,Technology University of Eindhoven,Eindhoven 5600MB,the Netherlands

出　　处：《Journal of Intelligent and Connected Vehicles》2024年第2期97-107,共11页智能网联汽车（英文）

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.51975088 and 51975089).

摘　　要：Positioning and mapping technology is a difficult and hot topic in autonomous driving environment sensing systems.In a complex traffic environment,the signal of the Global Navigation Satellite System(GNSS)will be blocked,leading to inaccurate vehicle positioning.To ensure the security of automatic electric campus vehicles,this study is based on the Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain(LEGO-LOAM)algorithm with a monocular vision system added.An algorithm framework based on Lidar-IMU-Camera(Lidar means light detection and ranging)fusion was proposed.A lightweight monocular vision odometer model was used,and the LEGO-LOAM system was employed to initialize monocular vision.The visual odometer information was taken as the initial value of the laser odometer.At the back-end opti9mization phase error state,the Kalman filtering fusion algorithm was employed to fuse the visual odometer and LEGO-LOAM system for positioning.The visual word bag model was applied to perform loopback detection.Taking the test results into account,the laser radar loopback detection was further optimized,reducing the accumulated positioning error.The real car experiment results showed that our algorithm could improve the mapping quality and positioning accuracy in the campus environment.The Lidar-IMU-Camera algorithm framework was verified on the Hong Kong city dataset UrbanNav.Compared with the LEGO-LOAM algorithm,the results show that the proposed algorithm can effectively reduce map drift,improve map resolution,and output more accurate driving trajectory information.

关 键 词：Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain(LEGO-LOAM) monocular vision system error state Kalman filter ODOMETER 

分 类 号：TN96[电子电信—信号与信息处理] U46[电子电信—信息与通信工程]