{"title":"Re-localization for Self-Driving Cars using Semantic Maps","authors":"Lhilo Kenye, Rishitha Palugulla, Mehul Arora, Bharath Bhat, R. Kala, Abhijeet Nayak","doi":"10.1109/IRC.2020.00018","DOIUrl":null,"url":null,"abstract":"Localization of vehicle in adverse conditions such as in dense traffic conditions is a challenging problem and state-of-the-art techniques often make the vehicle get lost, requiring a re-localization technique to correctly reset the vehicle pose. The visual place recognition and loop-closure based re-localization techniques need to store a very large map and take a lot of time to re-localize the vehicle. We solve the problem by making a semantic map which is used to re-localize the vehicle, if and once it gets lost by conventional localization techniques. The semantic map is created using a test vehicle with sophisticated sensors, and the map can be used by any vehicle with a stereo camera for re-localization. It is assumed that the test vehicle has a budget stereo camera which produces numerous false positives to be rejected by the re-localizer; while the vehicle also misses many key landmarks during the run due to heavy traffic. These are the challenges which are overcome by the designed re-localization algorithm. The vehicle is tested on a highway scenario in Bengaluru, India for multiple runs in a highway segment. Results confirm accurate re-localization on a semantic map generated from road-signs.","PeriodicalId":232817,"journal":{"name":"2020 Fourth IEEE International Conference on Robotic Computing (IRC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 Fourth IEEE International Conference on Robotic Computing (IRC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IRC.2020.00018","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Localization of vehicle in adverse conditions such as in dense traffic conditions is a challenging problem and state-of-the-art techniques often make the vehicle get lost, requiring a re-localization technique to correctly reset the vehicle pose. The visual place recognition and loop-closure based re-localization techniques need to store a very large map and take a lot of time to re-localize the vehicle. We solve the problem by making a semantic map which is used to re-localize the vehicle, if and once it gets lost by conventional localization techniques. The semantic map is created using a test vehicle with sophisticated sensors, and the map can be used by any vehicle with a stereo camera for re-localization. It is assumed that the test vehicle has a budget stereo camera which produces numerous false positives to be rejected by the re-localizer; while the vehicle also misses many key landmarks during the run due to heavy traffic. These are the challenges which are overcome by the designed re-localization algorithm. The vehicle is tested on a highway scenario in Bengaluru, India for multiple runs in a highway segment. Results confirm accurate re-localization on a semantic map generated from road-signs.