Since its introduction in 2014,the LiDAR odometry and mapping(LOAM)algorithm has become a cornerstone in the fields of autonomous driving and intelligent robotics.LOAM provides robust support for autonomous navigation...Since its introduction in 2014,the LiDAR odometry and mapping(LOAM)algorithm has become a cornerstone in the fields of autonomous driving and intelligent robotics.LOAM provides robust support for autonomous navigation in complex dynamic environments through precise localization and environmental mapping.This paper offers a comprehensive review of the innovations and optimizations made to the LOAM algorithm,covering advancements in multi-sensor fusion technology,frontend processing optimization,backend optimization,and loop closure detection.These improvements have significantly enhanced LOAM's performance in various scenarios,including urban,agricultural,and underground environments.However,challenges remain in areas such as data synchronization,real-time processing,computational complexity,and environmental adaptability.Looking ahead,future developments are expected to focus on creating more efficient multi-sensor fusion algorithms,expanding application domains,and building more robust systems,thereby driving continued progress in autonomous driving,intelligent robotics,and autonomous unmanned systems.展开更多
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 bloc...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.展开更多
基金supported by Jiangsu Agriculture Science and Technology Innovation Fund(CX(23)2003)China Agriculture Research System of MOF and MARA(CARS-28-21)+2 种基金the National Natural Science Foundation of China(32201680)the National Science and Technology Development Program of China(NK2022160104)the National Key Research and Development Program of China(2022YFD2001400).
文摘Since its introduction in 2014,the LiDAR odometry and mapping(LOAM)algorithm has become a cornerstone in the fields of autonomous driving and intelligent robotics.LOAM provides robust support for autonomous navigation in complex dynamic environments through precise localization and environmental mapping.This paper offers a comprehensive review of the innovations and optimizations made to the LOAM algorithm,covering advancements in multi-sensor fusion technology,frontend processing optimization,backend optimization,and loop closure detection.These improvements have significantly enhanced LOAM's performance in various scenarios,including urban,agricultural,and underground environments.However,challenges remain in areas such as data synchronization,real-time processing,computational complexity,and environmental adaptability.Looking ahead,future developments are expected to focus on creating more efficient multi-sensor fusion algorithms,expanding application domains,and building more robust systems,thereby driving continued progress in autonomous driving,intelligent robotics,and autonomous unmanned systems.
基金supported by the National Natural Science Foundation of China(Grant Nos.51975088 and 51975089).
文摘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.
