2013 IEEE Workshop on Robot Vision (WORV)最新文献_第4页

Segment-based robotic mapping in dynamic environments 动态环境中基于分段的机器人映射

2013 IEEE Workshop on Robot Vision (WORV) Pub Date : 2013-05-30 DOI: 10.1109/WORV.2013.6521913

Ross T. Creed, R. Lakaemper

引用次数: 2

Why would i want a gyroscope on my RGB-D sensor? 我为什么要在我的RGB-D传感器上安装陀螺仪?

2013 IEEE Workshop on Robot Vision (WORV) Pub Date : 2013-01-01 DOI: 10.1109/WORV.2013.6521916

H. Ovrén, Per-Erik Forssén, D. Tornqvist

引用次数: 16

Visual-inertial navigation with guaranteed convergence 具有保证收敛性的视觉惯性导航

2013 IEEE Workshop on Robot Vision (WORV) Pub Date : 1900-01-01 DOI: 10.1109/WORV.2013.6521930

F. Di Corato, M. Innocenti, L. Pollini

引用次数: 4

Clustering of image features based on contact and occlusion among robot body and objects 基于机器人身体和物体之间的接触和遮挡的图像特征聚类

2013 IEEE Workshop on Robot Vision (WORV) Pub Date : 1900-01-01 DOI: 10.1109/WORV.2013.6521939

T. Somei, Y. Kobayashi, A. Shimizu, T. Kaneko

引用次数: 3

RMSD: A 3D real-time mid-level scene description system RMSD:一个三维实时中层场景描述系统

2013 IEEE Workshop on Robot Vision (WORV) Pub Date : 1900-01-01 DOI: 10.1007/978-3-662-43859-6_2

K. Georgiev, R. Lakaemper

引用次数: 1

Real-time obstacle detection and avoidance in the presence of specular surfaces using an active 3D sensor 实时障碍物检测和避免在镜面的存在使用一个主动的3D传感器

2013 IEEE Workshop on Robot Vision (WORV) Pub Date : 1900-01-01 DOI: 10.1109/WORV.2013.6521938

B. Peasley, Stan Birchfield

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引用次数: 44

Probabilistic analysis of incremental light bundle adjustment 增量光束调整的概率分析

2013 IEEE Workshop on Robot Vision (WORV) Pub Date : 1900-01-01 DOI: 10.1109/WORV.2013.6521942

Vadim Indelman, Richard Roberts, F. Dellaert

{"title":"Probabilistic analysis of incremental light bundle adjustment","authors":"Vadim Indelman, Richard Roberts, F. Dellaert","doi":"10.1109/WORV.2013.6521942","DOIUrl":"https://doi.org/10.1109/WORV.2013.6521942","url":null,"abstract":"This paper presents a probabilistic analysis of the recently introduced incremental light bundle adjustment method (iLBA) [6]. In iLBA, the observed 3D points are algebraically eliminated, resulting in a cost function with only the camera poses as variables, and an incremental smoothing technique is applied for efficiently processing incoming images. While we have already showed that compared to conventional bundle adjustment (BA), iLBA yields a significant improvement in computational complexity with similar levels of accuracy, the probabilistic properties of iLBA have not been analyzed thus far. In this paper we consider the probability distribution that corresponds to the iLBA cost function, and analyze how well it represents the true density of the camera poses given the image measurements. The latter can be exactly calculated in bundle adjustment (BA) by marginalizing out the 3D points from the joint distribution of camera poses and 3D points. We present a theoretical analysis of the differences in the way that LBA and BA use measurement information. Using indoor and outdoor datasets we show that the first two moments of the iLBA and the true probability distributions are very similar in practice.","PeriodicalId":130461,"journal":{"name":"2013 IEEE Workshop on Robot Vision (WORV)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134196796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6