Hyun-Yong Jeon;Minseong Choi;Yeongseok Lee;Sangyoon Oh;Seunghoon Yang;Keun Ha Choi;Kyung-Soo Kim
{"title":"POW4R:点明智的全速估计使用四维雷达-相机融合超越径向限制","authors":"Hyun-Yong Jeon;Minseong Choi;Yeongseok Lee;Sangyoon Oh;Seunghoon Yang;Keun Ha Choi;Kyung-Soo Kim","doi":"10.1109/LRA.2025.3606356","DOIUrl":null,"url":null,"abstract":"This letter proposes an algorithm for full-velocity estimation by fusing radial velocity vectors obtained from 4D radar with optical flow vectors extracted from camera images. The full-velocity algorithm consists of a preprocessing step and a full-velocity vector estimation step. In preprocessing, radar noise is removed and ego-vehicle velocity estimation is enhanced using a Hampel filter for improved robustness in dynamic environments. In the full-velocity estimation stage, the algorithm estimates full-velocity vectors using a formulation derived from mathematical equations that incorporate multiple constraints. To evaluate the proposed method, an embedded system is implemented on a real vehicle, and datasets are collected under various scenarios. Experimental results show that the proposed algorithm significantly improves object velocity estimation performance. (error rate: baseline 81% <inline-formula><tex-math>$\\rightarrow$</tex-math></inline-formula> proposed 31%).","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"10 10","pages":"10934-10941"},"PeriodicalIF":5.3000,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11150770","citationCount":"0","resultStr":"{\"title\":\"POW4R: POint-Wise Full-Velocity Estimation Using 4D Radar-Camera Fusion Beyond Radial Limitations\",\"authors\":\"Hyun-Yong Jeon;Minseong Choi;Yeongseok Lee;Sangyoon Oh;Seunghoon Yang;Keun Ha Choi;Kyung-Soo Kim\",\"doi\":\"10.1109/LRA.2025.3606356\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This letter proposes an algorithm for full-velocity estimation by fusing radial velocity vectors obtained from 4D radar with optical flow vectors extracted from camera images. The full-velocity algorithm consists of a preprocessing step and a full-velocity vector estimation step. In preprocessing, radar noise is removed and ego-vehicle velocity estimation is enhanced using a Hampel filter for improved robustness in dynamic environments. In the full-velocity estimation stage, the algorithm estimates full-velocity vectors using a formulation derived from mathematical equations that incorporate multiple constraints. To evaluate the proposed method, an embedded system is implemented on a real vehicle, and datasets are collected under various scenarios. Experimental results show that the proposed algorithm significantly improves object velocity estimation performance. (error rate: baseline 81% <inline-formula><tex-math>$\\\\rightarrow$</tex-math></inline-formula> proposed 31%).\",\"PeriodicalId\":13241,\"journal\":{\"name\":\"IEEE Robotics and Automation Letters\",\"volume\":\"10 10\",\"pages\":\"10934-10941\"},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2025-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11150770\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Robotics and Automation Letters\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11150770/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ROBOTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Robotics and Automation Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/11150770/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ROBOTICS","Score":null,"Total":0}
This letter proposes an algorithm for full-velocity estimation by fusing radial velocity vectors obtained from 4D radar with optical flow vectors extracted from camera images. The full-velocity algorithm consists of a preprocessing step and a full-velocity vector estimation step. In preprocessing, radar noise is removed and ego-vehicle velocity estimation is enhanced using a Hampel filter for improved robustness in dynamic environments. In the full-velocity estimation stage, the algorithm estimates full-velocity vectors using a formulation derived from mathematical equations that incorporate multiple constraints. To evaluate the proposed method, an embedded system is implemented on a real vehicle, and datasets are collected under various scenarios. Experimental results show that the proposed algorithm significantly improves object velocity estimation performance. (error rate: baseline 81% $\rightarrow$ proposed 31%).
期刊介绍:
The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.