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

TL;DR

This paper introduces GCSLAM and SF-Loc, a semantic SLAM and localization system that achieves accurate mapping and robust localization in complex indoor parking environments using multi-sensor data and semantic constraints.

Contribution

The work presents a novel globally consistent semantic SLAM system with a semantic-constrained factor graph and a semantic-fusion localization subsystem leveraging semantic maps.

Findings

Outperforms existing SLAM methods on real-world parking datasets

Achieves high accuracy in semantic mapping and localization

Demonstrates robustness in complex indoor parking scenarios

Abstract

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.