# Towards Autonomous Indoor Parking: A Globally Consistent Semantic SLAM System and A Semantic Localization Subsystem

arXiv: https://arxiv.org/abs/2410.12169 · HTML: https://mlsanity.com/paper/2410.12169/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2024.

## Built on by (most cited first, 1 of 1)

- [SGLoc: Semantic Localization System for Camera Pose Estimation from 3D Gaussian Splatting Representation](https://mlsanity.com/paper/2507.12027/) (2025)

## Abstract (arXiv)

We propose a globally consistent semantic SLAM system (GCSLAM) and a semantic-fusion localization subsystem (SF-Loc), which achieves accurate semantic mapping and robust localization in complex parking lots. Visual cameras (front-view and surround-view), IMU, and wheel encoder form the input sensor configuration of our system. The first part of our work is GCSLAM. GCSLAM introduces a semantic-constrained factor graph for the optimization of poses and semantic map, which incorporates innovative error terms based on multi-sensor data and BEV (bird's-eye view) semantic information. Additionally, GCSLAM integrates a Global Slot Management module that stores and manages parking slot observations. SF-Loc is the second part of our work, which leverages the semantic map built by GCSLAM to conduct map-based localization. SF-Loc integrates registration results and odometry poses with a novel factor graph. Our system demonstrates superior performance over existing SLAM on two real-world datasets, showing excellent capabilities in robust global localization and precise semantic mapping.