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Hybrid Camera Pose Estimation With Online Partitioning for SLAM

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This letter presents a hybrid real-time camera pose estimation framework with a novel partitioning scheme and introduces motion averaging to monocular Simultaneous Localization and Mapping (SLAM) systems. Breaking through the… Click to show full abstract

This letter presents a hybrid real-time camera pose estimation framework with a novel partitioning scheme and introduces motion averaging to monocular Simultaneous Localization and Mapping (SLAM) systems. Breaking through the limitations of fixed-size temporal partitioning in many conventional SLAM pipelines, our approach significantly improves the accuracy of local bundle adjustment by gathering spatially-strongly-connected cameras into each block. With the dynamic initialization using intermediate computation values, we improve the Levenberg-Marquardt solver to further enhance the efficiency of the local optimization. Moreover, the dense data association between blocks by our co-visibility-based partitioning enables us to explore and implement motion averaging to efficiently align the blocks globally, updating camera motion estimations on-the-fly. Experiments on benchmarks convincingly demonstrate the practicality and robustness of our proposed approach by significantly outperforming conventional approaches.

Keywords: hybrid camera; camera pose; pose estimation; estimation online; camera

Journal Title: IEEE Robotics and Automation Letters
Year Published: 2020

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