6533b82dfe1ef96bd1291fd2

RESEARCH PRODUCT

Robust Selective Stereo SLAM without Loop Closure and Bundle Adjustment

Carlo ColomboFabio BellaviaFabio PazzagliaMarco Fanfani

subject

feature matchingSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniScheme (programming language)RANSACSettore INF/01 - InformaticaMatching (graph theory)business.industryFrame (networking)Bundle adjustmentTracking (particle physics)Structure from MotionLoop (topology)Flow (mathematics)SLAMComputer visionframe selectionArtificial intelligencebusinessPosecomputerVisual SLAMMathematicscomputer.programming_language

description

This paper presents a novel stereo SLAM framework, where a robust loop chain matching scheme for tracking keypoints is combined with an effective frame selection strategy. The proposed approach, referred to as selective SLAM (SSLAM), relies on the observation that the error in the pose estimation propagates from the uncertainty of the three-dimensional points. This is higher for distant points, corresponding to matches with low temporal flow disparity in the images. Comparative results based on the reference KITTI evaluation framework show that SSLAM is effective and can be implemented efficiently, as it does not require any loop closure or bundle adjustment.

yearjournalcountryeditionlanguage
2013-01-01
https://doi.org/10.1007/978-3-642-41181-6_47