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Trajectory-Driven Point Cloud Compression Techniques for Visual SLAM

Luis Contreras Toledo, Walterio Mayol-Cuevas, Trajectory-Driven Point Cloud Compression Techniques for Visual SLAM. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). October 2015. PDF, 2461 Kbytes.


We develop and evaluate methods based on a novel data compression strategy for visual SLAM that uses travelled 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 intrinsic to the trajectory of exploration together with the visual information of map points. We perform rigorous statistical evaluation and Pareto analysis to show how this approach compares with three widely used baseline compression methods: k-means on point geometry, keyframes and random sampling. The result of this work shows that compressing maps to levels of 25% or even less of the original data is possible, while preserving good 6D visual relocalisation performance.

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