# Coarse-To-Fine Visual Localization Using Semantic Compact Map

arXiv: https://arxiv.org/abs/1910.04936 · HTML: https://mlsanity.com/paper/1910.04936/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2019.

## Built on by (most cited first, 3 of 3)

- [You Only Label Once: 3D Box Adaptation from Point Cloud to Image via Semi-Supervised Learning](https://mlsanity.com/paper/2211.09302/) (2022)
- [Monocular Localization with Semantics Map for Autonomous Vehicles](https://mlsanity.com/paper/2406.03835/) (2024)
- [Map-Free Visual Relocalization Enhanced by Instance Knowledge and Depth Knowledge](https://mlsanity.com/paper/2408.13085/) (2024)

## Abstract (arXiv)

Robust visual localization for urban vehicles remains challenging and unsolved. The limitation of computation efficiency and memory size has made it harder for large-scale applications. Since semantic information serves as a stable and compact representation of the environment, we propose a coarse-to-fine localization system based on a semantic compact map. Pole-like objects are stored in the compact map, then are extracted from semantically segmented images as observations. Localization is performed by a particle filter, followed by a pose alignment module decoupling translation and rotation to achieve better accuracy. We evaluate our system both on synthetic and realistic datasets and compare it with two baselines, a state-of-art semantic feature-based system, and a traditional SIFT feature-based system. Experiments demonstrate that even with a significantly small map, such as a 10 KB map for a 3.7 km long trajectory, our system provides a comparable accuracy with the baselines.