  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.
