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This paper presents O-POCO, a visual odometry and SLAM system that makes online decisions regarding what to map and what to ignore. It takes a point cloud from classical SfM and aims to sample it on-line by selecting map features useful for future 6D relocalisation. We use the camera's traveled trajectory to compartamentalize the point cloud, along with visual and spatial information to sample and...
We develop and evaluate methods based on a novel data compression strategy for visual SLAM that uses traveled trajectory analysis. Beyond compressing scene structure based purely on geometry, we aim at developing compact map representations that are useful for re-exploration while preserving scene structure. Our work is evaluated on data collected from a visual sensor and exploits the information...
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