# Hierarchical Segment-based Optimization for SLAM

arXiv: https://arxiv.org/abs/2111.04101 · HTML: https://mlsanity.com/paper/2111.04101/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 7 Nov 2021.

## Builds on (most cited first, 7 of 7)

- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [OpenVSLAM: A Versatile Visual SLAM Framework](https://mlsanity.com/paper/1910.01122/) (2019)
- [Stochastic Bundle Adjustment for Efficient and Scalable 3D Reconstruction](https://mlsanity.com/paper/2008.00446/) (2020)
- [ENFT: Efficient Non-Consecutive Feature Tracking for Robust Structure-from-Motion](https://mlsanity.com/paper/1510.08012/) (2015)
- [Hybrid Camera Pose Estimation with Online Partitioning for SLAM](https://mlsanity.com/paper/1908.01797/) (2019)
- [Multistage SFM: A Coarse-to-Fine Approach for 3D Reconstruction](https://mlsanity.com/paper/1512.06235/) (2015)
- [Pose Correction Algorithm for Relative Frames between Keyframes in SLAM](https://mlsanity.com/paper/2009.08724/) (2020)

## Abstract (arXiv)

This paper presents a hierarchical segment-based optimization method for Simultaneous Localization and Mapping (SLAM) system. First we propose a reliable trajectory segmentation method that can be used to increase efficiency in the back-end optimization. Then we propose a buffer mechanism for the first time to improve the robustness of the segmentation. During the optimization, we use global information to optimize the frames with large error, and interpolation instead of optimization to update well-estimated frames to hierarchically allocate the amount of computation according to error of each frame. Comparative experiments on the benchmark show that our method greatly improves the efficiency of optimization with almost no drop in accuracy, and outperforms existing high-efficiency optimization method by a large margin.