Hybrid Camera Pose Estimation with Online Partitioning for SLAM
This work addresses accuracy limitations in real-time SLAM systems for robotics and AR/VR applications, representing an incremental improvement with novel partitioning and optimization techniques.
The paper tackles the problem of improving camera pose estimation accuracy in monocular SLAM systems by introducing a novel online partitioning scheme that groups spatially-connected cameras, which significantly outperforms conventional approaches in benchmark experiments.
This paper 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, \XL{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.