{"title":"LiDAR odometry survey: recent advancements and remaining challenges","authors":"","doi":"10.1007/s11370-024-00515-8","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>Odometry is crucial for robot navigation, particularly in situations where global positioning methods like global positioning system are unavailable. The main goal of odometry is to predict the robot’s motion and accurately determine its current location. Various sensors, such as wheel encoder, inertial measurement unit (IMU), camera, radar, and Light Detection and Ranging (LiDAR), are used for odometry in robotics. LiDAR, in particular, has gained attention for its ability to provide rich three-dimensional (3D) data and immunity to light variations. This survey aims to examine advancements in LiDAR odometry thoroughly. We start by exploring LiDAR technology and then scrutinize LiDAR odometry works, categorizing them based on their sensor integration approaches. These approaches include methods relying solely on LiDAR, those combining LiDAR with IMU, strategies involving multiple LiDARs, and methods fusing LiDAR with other sensor modalities. In conclusion, we address existing challenges and outline potential future directions in LiDAR odometry. Additionally, we analyze public datasets and evaluation methods for LiDAR odometry. To our knowledge, this survey is the first comprehensive exploration of LiDAR odometry.</p>","PeriodicalId":48813,"journal":{"name":"Intelligent Service Robotics","volume":"97 1","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Intelligent Service Robotics","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11370-024-00515-8","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 0
Abstract
Odometry is crucial for robot navigation, particularly in situations where global positioning methods like global positioning system are unavailable. The main goal of odometry is to predict the robot’s motion and accurately determine its current location. Various sensors, such as wheel encoder, inertial measurement unit (IMU), camera, radar, and Light Detection and Ranging (LiDAR), are used for odometry in robotics. LiDAR, in particular, has gained attention for its ability to provide rich three-dimensional (3D) data and immunity to light variations. This survey aims to examine advancements in LiDAR odometry thoroughly. We start by exploring LiDAR technology and then scrutinize LiDAR odometry works, categorizing them based on their sensor integration approaches. These approaches include methods relying solely on LiDAR, those combining LiDAR with IMU, strategies involving multiple LiDARs, and methods fusing LiDAR with other sensor modalities. In conclusion, we address existing challenges and outline potential future directions in LiDAR odometry. Additionally, we analyze public datasets and evaluation methods for LiDAR odometry. To our knowledge, this survey is the first comprehensive exploration of LiDAR odometry.
期刊介绍：
The journal directs special attention to the emerging significance of integrating robotics with information technology and cognitive science (such as ubiquitous and adaptive computing,information integration in a distributed environment, and cognitive modelling for human-robot interaction), which spurs innovation toward a new multi-dimensional robotic service to humans. The journal intends to capture and archive this emerging yet significant advancement in the field of intelligent service robotics. The journal will publish original papers of innovative ideas and concepts, new discoveries and improvements, as well as novel applications and business models which are related to the field of intelligent service robotics described above and are proven to be of high quality. The areas that the Journal will cover include, but are not limited to: Intelligent robots serving humans in daily life or in a hazardous environment, such as home or personal service robots, entertainment robots, education robots, medical robots, healthcare and rehabilitation robots, and rescue robots (Service Robotics); Intelligent robotic functions in the form of embedded systems for applications to, for example, intelligent space, intelligent vehicles and transportation systems, intelligent manufacturing systems, and intelligent medical facilities (Embedded Robotics); The integration of robotics with network technologies, generating such services and solutions as distributed robots, distance robotic education-aides, and virtual laboratories or museums (Networked Robotics).
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