{"title":"Pole-based Localization for Autonomous Vehicles in Urban Scenarios Using Local Grid Map-based Method","authors":"Fan Lu, Guang Chen, Jinhu Dong, Xiaoding Yuan, Shangding Gu, Alois Knoll","doi":"10.1109/ICARM49381.2020.9195330","DOIUrl":null,"url":null,"abstract":"Self-localization is a key component of autonomous vehicles in urban scenarios. In this work, we proposed a localization system which is based on pole-like objects such as trees and street lamps. Pole-like objects are extracted from 3D LiDAR point cloud using a cluster-based method. Based on the pole detection results, we propose a new map representation which consists of numerous local grid maps. In order to tackle the data association problem caused by the ambiguity of pole-like landmarks, the detected poles are directly transformed to the local grid map to define a cost function without pole-to-pole matching. The subsequent non-linear optimization method is utilized to minimize the cost function and generate the vehicle pose. We evaluate our localization system on our self-collected dataset. And the proposed system achieves a root mean square error of less than 18 cm for position and less than 0.52 ° for yaw.","PeriodicalId":189668,"journal":{"name":"2020 5th International Conference on Advanced Robotics and Mechatronics (ICARM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 5th International Conference on Advanced Robotics and Mechatronics (ICARM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICARM49381.2020.9195330","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
Self-localization is a key component of autonomous vehicles in urban scenarios. In this work, we proposed a localization system which is based on pole-like objects such as trees and street lamps. Pole-like objects are extracted from 3D LiDAR point cloud using a cluster-based method. Based on the pole detection results, we propose a new map representation which consists of numerous local grid maps. In order to tackle the data association problem caused by the ambiguity of pole-like landmarks, the detected poles are directly transformed to the local grid map to define a cost function without pole-to-pole matching. The subsequent non-linear optimization method is utilized to minimize the cost function and generate the vehicle pose. We evaluate our localization system on our self-collected dataset. And the proposed system achieves a root mean square error of less than 18 cm for position and less than 0.52 ° for yaw.