Search results for " ransac"

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Accurate keyframe selection and keypoint tracking for robust visual odometry

2016

This paper presents a novel stereo visual odometry (VO) framework based on structure from motion, where a robust keypoint tracking and matching is combined with an effective keyframe selection strategy. In order to track and find correct feature correspondences a robust loop chain matching scheme on two consecutive stereo pairs is introduced. Keyframe selection is based on the proportion of features with high temporal disparity. This criterion relies on the observation that the error in the pose estimation propagates from the uncertainty of 3D points—higher for distant points, that have low 2D motion. Comparative results based on three VO datasets show that the proposed solution is remarkab…

0209 industrial biotechnologyMatching (graph theory)Computer scienceVisual odometryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION02 engineering and technologyKeyframe selectionRANSAC020901 industrial engineering & automation0202 electrical engineering electronic engineering information engineeringStructure from motionComputer visionVisual odometryVisual Odometry Structure from Motion RANSAC feature matching keyframe selectionPoseSelection (genetic algorithm)Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniRANSACSettore INF/01 - InformaticaFeature matchingbusiness.industryStructure from motionPattern recognitionComputer Science ApplicationsHardware and ArchitectureFeature (computer vision)Pattern recognition (psychology)020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligencebusinessSoftware
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noRANSAC for fundamental matrix estimation

2011

The estimation of the fundamental matrix from a set of corresponding points is a relevant topic in epipolar stereo geometry [10]. Due to the high amount of outliers between the matches, RANSAC-based approaches [7, 13, 29] have been used to obtain the fundamental matrix. In this paper two new contributes are presented: a new normalized epipolar error measure which takes into account the shape of the features used as matches [17] and a new strategy to compare fundamental matrices. The proposed error measure gives good results and it does not depend on the image scale. Moreover, the new evaluation strategy describes a valid tool to compare diffe rent RANSAC-based methods because it does not re…

Evaluation strategyGround truthSettore INF/01 - Informaticabusiness.industryimage features epipolar geometry ransac fundamental matrix estimationEight-point algorithmEpipolar geometryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage scaleRANSACOutlierComputer visionArtificial intelligencebusinessFundamental matrix (computer vision)AlgorithmMathematicsProcedings of the British Machine Vision Conference 2011
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