{"title":"Depth estimation to manage visual signal loss during visual servoing with a 3 DOF camera","authors":"A. D. Petiteville, V. Cadenat, M. Courdesses","doi":"10.1109/ECMSM.2013.6648953","DOIUrl":null,"url":null,"abstract":"This paper deals with the problem of estimating the visual features during a vision-based navigation task when a temporary total occlusion occurs. The proposed approach relies on an existent specific algorithm. However, to be efficient, this algorithm requires highly precise initial values for both the image features and their depth. Thus, our objective is to design a predictor/estimator pair able to provide an accurate estimation of the depth value, even when the visual data are noisy. We also aim at obtaining a method reducing the implementation complexity while preserving performances. The obtained results show the efficiency and the interest of our technique.","PeriodicalId":174767,"journal":{"name":"2013 IEEE 11th International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics","volume":"1 4","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE 11th International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ECMSM.2013.6648953","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper deals with the problem of estimating the visual features during a vision-based navigation task when a temporary total occlusion occurs. The proposed approach relies on an existent specific algorithm. However, to be efficient, this algorithm requires highly precise initial values for both the image features and their depth. Thus, our objective is to design a predictor/estimator pair able to provide an accurate estimation of the depth value, even when the visual data are noisy. We also aim at obtaining a method reducing the implementation complexity while preserving performances. The obtained results show the efficiency and the interest of our technique.