Filippo Volpin, S. Chiodini, Simone Fortuna, A. Valmorbida, M. Pertile
{"title":"360度视场扫描激光雷达与非重复扫描激光雷达:漫游者导航实验","authors":"Filippo Volpin, S. Chiodini, Simone Fortuna, A. Valmorbida, M. Pertile","doi":"10.1109/MetroAeroSpace57412.2023.10189983","DOIUrl":null,"url":null,"abstract":"This paper presents an experimental comparison of two types of LiDAR sensors for the navigation of rovers: a 360-deg FOV Scanning LiDAR, the Ouster OS1-32, and a non-repetitive scanning LiDAR, the Livox Horizon. The study aims to determine the performance differences between these two sensors in terms of accuracy, and reliability, which are crucial factors for rover navigation in complex environments. The data collected by the LiDAR were processed as follows: the trajectory was reconstructed using the LiDAR-SLAM algorithm FAST-LIO and the obstacle map was created using the VoxBlox library. The experiments were conducted in various scenarios, including terrain with obstacles and uneven surfaces, and the results were analyzed in terms of trajectory error.","PeriodicalId":153093,"journal":{"name":"2023 IEEE 10th International Workshop on Metrology for AeroSpace (MetroAeroSpace)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"360-deg FOV Scanning LiDAR versus Non-Repetitive Scanning LiDAR: A Rover Navigation Experiment\",\"authors\":\"Filippo Volpin, S. Chiodini, Simone Fortuna, A. Valmorbida, M. Pertile\",\"doi\":\"10.1109/MetroAeroSpace57412.2023.10189983\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an experimental comparison of two types of LiDAR sensors for the navigation of rovers: a 360-deg FOV Scanning LiDAR, the Ouster OS1-32, and a non-repetitive scanning LiDAR, the Livox Horizon. The study aims to determine the performance differences between these two sensors in terms of accuracy, and reliability, which are crucial factors for rover navigation in complex environments. The data collected by the LiDAR were processed as follows: the trajectory was reconstructed using the LiDAR-SLAM algorithm FAST-LIO and the obstacle map was created using the VoxBlox library. The experiments were conducted in various scenarios, including terrain with obstacles and uneven surfaces, and the results were analyzed in terms of trajectory error.\",\"PeriodicalId\":153093,\"journal\":{\"name\":\"2023 IEEE 10th International Workshop on Metrology for AeroSpace (MetroAeroSpace)\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE 10th International Workshop on Metrology for AeroSpace (MetroAeroSpace)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MetroAeroSpace57412.2023.10189983\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE 10th International Workshop on Metrology for AeroSpace (MetroAeroSpace)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MetroAeroSpace57412.2023.10189983","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
360-deg FOV Scanning LiDAR versus Non-Repetitive Scanning LiDAR: A Rover Navigation Experiment
This paper presents an experimental comparison of two types of LiDAR sensors for the navigation of rovers: a 360-deg FOV Scanning LiDAR, the Ouster OS1-32, and a non-repetitive scanning LiDAR, the Livox Horizon. The study aims to determine the performance differences between these two sensors in terms of accuracy, and reliability, which are crucial factors for rover navigation in complex environments. The data collected by the LiDAR were processed as follows: the trajectory was reconstructed using the LiDAR-SLAM algorithm FAST-LIO and the obstacle map was created using the VoxBlox library. The experiments were conducted in various scenarios, including terrain with obstacles and uneven surfaces, and the results were analyzed in terms of trajectory error.