Global Navigation Satellite System(GNSS)can provide all-weather,all-time,high-precision positioning,navigation and timing services,which plays an important role in national security,national economy,public life and ot...Global Navigation Satellite System(GNSS)can provide all-weather,all-time,high-precision positioning,navigation and timing services,which plays an important role in national security,national economy,public life and other aspects.However,in environments with limited satellite signals such as urban canyons,tunnels,and indoor spaces,it is difficult to provide accurate and reliable positioning services only by satellite navigation.Multi-source sensor integrated navigation can effectively overcome the limitations of single-sensor navigation through the fusion of different types of sensor data such as Inertial Measurement Unit(IMU),vision sensor,and LiDAR,and provide more accurate,stable and robust navigation information in complex environments.We summarizes the research status of multi-source sensor integrated navigation technology,and focuses on the representative innovations and applications of integrated navigation and positioning technology by major domestic scientific research institutions in China during 2019—2023.展开更多
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
In recent years,Simultaneous Localization And Mapping(SLAM)technology has prevailed in a wide range of applications,such as autonomous driving,intelligent robots,Augmented Reality(AR),and Virtual Reality(VR).Multi-sen...In recent years,Simultaneous Localization And Mapping(SLAM)technology has prevailed in a wide range of applications,such as autonomous driving,intelligent robots,Augmented Reality(AR),and Virtual Reality(VR).Multi-sensor fusion using the most popular three types of sensors(e.g.,visual sensor,LiDAR sensor,and IMU)is becoming ubiquitous in SLAM,in part because of the complementary sensing capabilities and the inevitable shortages(e.g.,low precision and long-term drift)of the stand-alone sensor in challenging environments.In this article,we survey thoroughly the research efforts taken in this field and strive to provide a concise but complete review of the related work.Firstly,a brief introduction of the state estimator formation in SLAM is presented.Secondly,the state-of-the-art algorithms of different multi-sensor fusion algorithms are given.Then we analyze the deficiencies associated with the reviewed approaches and formulate some future research considerations.This paper can be considered as a brief guide to newcomers and a comprehensive reference for experienced researchers and engineers to explore new interesting orientations.展开更多
基金National Key R&D Program of China(No.2021YFB2501102)。
文摘Global Navigation Satellite System(GNSS)can provide all-weather,all-time,high-precision positioning,navigation and timing services,which plays an important role in national security,national economy,public life and other aspects.However,in environments with limited satellite signals such as urban canyons,tunnels,and indoor spaces,it is difficult to provide accurate and reliable positioning services only by satellite navigation.Multi-source sensor integrated navigation can effectively overcome the limitations of single-sensor navigation through the fusion of different types of sensor data such as Inertial Measurement Unit(IMU),vision sensor,and LiDAR,and provide more accurate,stable and robust navigation information in complex environments.We summarizes the research status of multi-source sensor integrated navigation technology,and focuses on the representative innovations and applications of integrated navigation and positioning technology by major domestic scientific research institutions in China during 2019—2023.
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 Scientific and Technological Innovation 2030(No.2021ZD0110900).
文摘In recent years,Simultaneous Localization And Mapping(SLAM)technology has prevailed in a wide range of applications,such as autonomous driving,intelligent robots,Augmented Reality(AR),and Virtual Reality(VR).Multi-sensor fusion using the most popular three types of sensors(e.g.,visual sensor,LiDAR sensor,and IMU)is becoming ubiquitous in SLAM,in part because of the complementary sensing capabilities and the inevitable shortages(e.g.,low precision and long-term drift)of the stand-alone sensor in challenging environments.In this article,we survey thoroughly the research efforts taken in this field and strive to provide a concise but complete review of the related work.Firstly,a brief introduction of the state estimator formation in SLAM is presented.Secondly,the state-of-the-art algorithms of different multi-sensor fusion algorithms are given.Then we analyze the deficiencies associated with the reviewed approaches and formulate some future research considerations.This paper can be considered as a brief guide to newcomers and a comprehensive reference for experienced researchers and engineers to explore new interesting orientations.
