Trajectory-driven point cloud compression techniques for visual SLAM

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 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. Results indicate that compressing maps to levels of 25% or even less of the original data is possible, while preserving good 6D visual relocalisation performance.