{"title":"非朗伯表面及其对视觉 SLAM 的挑战","authors":"Sara Pyykölä;Niclas Joswig;Laura Ruotsalainen","doi":"10.1109/OJCS.2024.3419832","DOIUrl":null,"url":null,"abstract":"Non-Lambertian surfaces are special surfaces that can cause specific type of reflectances called specularities, which pose a potential issue in industrial SLAM. This article reviews fundamental surface reflectance models, modern state-of-the-art computer vision algorithms and two public datasets, KITTI and DiLiGenT, related to non-Lambertian surfaces' research. A new dataset, SPINS, is presented for the purpose of studying non-Lambertian surfaces in navigation and an empirical performance evaluation with ResNeXt-101-WSL, ORB SLAM 3 and TartanVO is performed on the data. The article concludes with discussion about the results of empirical evaluation and the findings of the survey.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"430-445"},"PeriodicalIF":0.0000,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10574359","citationCount":"0","resultStr":"{\"title\":\"Non-Lambertian Surfaces and Their Challenges for Visual SLAM\",\"authors\":\"Sara Pyykölä;Niclas Joswig;Laura Ruotsalainen\",\"doi\":\"10.1109/OJCS.2024.3419832\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Non-Lambertian surfaces are special surfaces that can cause specific type of reflectances called specularities, which pose a potential issue in industrial SLAM. This article reviews fundamental surface reflectance models, modern state-of-the-art computer vision algorithms and two public datasets, KITTI and DiLiGenT, related to non-Lambertian surfaces' research. A new dataset, SPINS, is presented for the purpose of studying non-Lambertian surfaces in navigation and an empirical performance evaluation with ResNeXt-101-WSL, ORB SLAM 3 and TartanVO is performed on the data. The article concludes with discussion about the results of empirical evaluation and the findings of the survey.\",\"PeriodicalId\":13205,\"journal\":{\"name\":\"IEEE Open Journal of the Computer Society\",\"volume\":\"5 \",\"pages\":\"430-445\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-06-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10574359\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Open Journal of the Computer Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10574359/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Journal of the Computer Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10574359/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
非朗伯表面是一种特殊的表面,会产生被称为 "镜面反射 "的特殊反射率,这给工业 SLAM 带来了潜在的问题。本文回顾了与非朗伯表面研究相关的基本表面反射模型、现代最先进的计算机视觉算法和两个公共数据集(KITTI 和 DiLiGenT)。文章介绍了一个新的数据集 SPINS,用于研究导航中的非朗伯表面,并使用 ResNeXt-101-WSL、ORB SLAM 3 和 TartanVO 对数据进行了实证性能评估。文章最后讨论了实证评估结果和调查结果。
Non-Lambertian Surfaces and Their Challenges for Visual SLAM
Non-Lambertian surfaces are special surfaces that can cause specific type of reflectances called specularities, which pose a potential issue in industrial SLAM. This article reviews fundamental surface reflectance models, modern state-of-the-art computer vision algorithms and two public datasets, KITTI and DiLiGenT, related to non-Lambertian surfaces' research. A new dataset, SPINS, is presented for the purpose of studying non-Lambertian surfaces in navigation and an empirical performance evaluation with ResNeXt-101-WSL, ORB SLAM 3 and TartanVO is performed on the data. The article concludes with discussion about the results of empirical evaluation and the findings of the survey.