{"title":"一种用于自动驾驶的模块化闭环检测方案——一种松耦合方法","authors":"Wuqi Wang;Haigen Min;Xia Wu;Yukun Fang;Guofa Li;Xiangmo Zhao","doi":"10.1109/TVT.2024.3523349","DOIUrl":null,"url":null,"abstract":"Efficient and precise loop closure detection is essential for both autonomous vehicles and robotics. Currently, loop closure detection technologies can recognize locations using the similarity between environmental measurements. However, the inherent errors in these measurements present a significant challenge to detection performance, which has not been adequately addressed in the previous literature. To address these issues, this paper proposes a novel modular loop closure detection scheme based on the temporal similarity of loop sequences to enhance the consistency of constructed maps and thereby improve detection performance. Specifically, the similarity and credibility of loop closure sequences, as well as their corresponding criteria, are defined based on the characteristics of loop closure occurrences in map construction. Fast filtering and accurate matching strategies have been developed based on the similarity and credibility of loop closure sequences. An independent recall strategy is then developed to mitigate the impact of measurement similarity errors on recall. Unlike traditional approaches, this method integrates temporal similarity with existing measurement similarity schemes in a loosely coupled manner, improving the performance and efficiency of loop closure detection in complex environments. The effectiveness of the proposed method is sufficiently validated using both the public KITTI dataset and real field-test vehicles. The results demonstrate significant improvements in loop closure detection, providing a solid foundation for the advancement of autonomous driving technologies.","PeriodicalId":13421,"journal":{"name":"IEEE Transactions on Vehicular Technology","volume":"74 5","pages":"7163-7177"},"PeriodicalIF":7.1000,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Modular Loop Closure Detection Scheme for Autonomous Driving: A Loosely Coupled Approach\",\"authors\":\"Wuqi Wang;Haigen Min;Xia Wu;Yukun Fang;Guofa Li;Xiangmo Zhao\",\"doi\":\"10.1109/TVT.2024.3523349\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Efficient and precise loop closure detection is essential for both autonomous vehicles and robotics. Currently, loop closure detection technologies can recognize locations using the similarity between environmental measurements. However, the inherent errors in these measurements present a significant challenge to detection performance, which has not been adequately addressed in the previous literature. To address these issues, this paper proposes a novel modular loop closure detection scheme based on the temporal similarity of loop sequences to enhance the consistency of constructed maps and thereby improve detection performance. Specifically, the similarity and credibility of loop closure sequences, as well as their corresponding criteria, are defined based on the characteristics of loop closure occurrences in map construction. Fast filtering and accurate matching strategies have been developed based on the similarity and credibility of loop closure sequences. An independent recall strategy is then developed to mitigate the impact of measurement similarity errors on recall. Unlike traditional approaches, this method integrates temporal similarity with existing measurement similarity schemes in a loosely coupled manner, improving the performance and efficiency of loop closure detection in complex environments. The effectiveness of the proposed method is sufficiently validated using both the public KITTI dataset and real field-test vehicles. The results demonstrate significant improvements in loop closure detection, providing a solid foundation for the advancement of autonomous driving technologies.\",\"PeriodicalId\":13421,\"journal\":{\"name\":\"IEEE Transactions on Vehicular Technology\",\"volume\":\"74 5\",\"pages\":\"7163-7177\"},\"PeriodicalIF\":7.1000,\"publicationDate\":\"2024-12-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Vehicular Technology\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10817108/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Vehicular Technology","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10817108/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
A Modular Loop Closure Detection Scheme for Autonomous Driving: A Loosely Coupled Approach
Efficient and precise loop closure detection is essential for both autonomous vehicles and robotics. Currently, loop closure detection technologies can recognize locations using the similarity between environmental measurements. However, the inherent errors in these measurements present a significant challenge to detection performance, which has not been adequately addressed in the previous literature. To address these issues, this paper proposes a novel modular loop closure detection scheme based on the temporal similarity of loop sequences to enhance the consistency of constructed maps and thereby improve detection performance. Specifically, the similarity and credibility of loop closure sequences, as well as their corresponding criteria, are defined based on the characteristics of loop closure occurrences in map construction. Fast filtering and accurate matching strategies have been developed based on the similarity and credibility of loop closure sequences. An independent recall strategy is then developed to mitigate the impact of measurement similarity errors on recall. Unlike traditional approaches, this method integrates temporal similarity with existing measurement similarity schemes in a loosely coupled manner, improving the performance and efficiency of loop closure detection in complex environments. The effectiveness of the proposed method is sufficiently validated using both the public KITTI dataset and real field-test vehicles. The results demonstrate significant improvements in loop closure detection, providing a solid foundation for the advancement of autonomous driving technologies.
期刊介绍:
The scope of the Transactions is threefold (which was approved by the IEEE Periodicals Committee in 1967) and is published on the journal website as follows: Communications: The use of mobile radio on land, sea, and air, including cellular radio, two-way radio, and one-way radio, with applications to dispatch and control vehicles, mobile radiotelephone, radio paging, and status monitoring and reporting. Related areas include spectrum usage, component radio equipment such as cavities and antennas, compute control for radio systems, digital modulation and transmission techniques, mobile radio circuit design, radio propagation for vehicular communications, effects of ignition noise and radio frequency interference, and consideration of the vehicle as part of the radio operating environment. Transportation Systems: The use of electronic technology for the control of ground transportation systems including, but not limited to, traffic aid systems; traffic control systems; automatic vehicle identification, location, and monitoring systems; automated transport systems, with single and multiple vehicle control; and moving walkways or people-movers. Vehicular Electronics: The use of electronic or electrical components and systems for control, propulsion, or auxiliary functions, including but not limited to, electronic controls for engineer, drive train, convenience, safety, and other vehicle systems; sensors, actuators, and microprocessors for onboard use; electronic fuel control systems; vehicle electrical components and systems collision avoidance systems; electromagnetic compatibility in the vehicle environment; and electric vehicles and controls.