# Hybrid Camera Pose Estimation with Online Partitioning for SLAM

arXiv: https://arxiv.org/abs/1908.01797 · HTML: https://mlsanity.com/paper/1908.01797/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2019.

## Built on by (most cited first, 4 of 4)

- [Rotation Coordinate Descent for Fast Globally Optimal Rotation Averaging](https://mlsanity.com/paper/2103.08292/) (2021)
- [Chordal Averaging on Flag Manifolds and Its Applications](https://mlsanity.com/paper/2303.13501/) (2023)
- [On the Robustness of Multi-View Rotation Averaging](https://mlsanity.com/paper/2102.05454/) (2021)
- [Hierarchical Segment-based Optimization for SLAM](https://mlsanity.com/paper/2111.04101/) (2021)

## Abstract (arXiv)

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, XLwe 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.