构建可全局一致的语义SLAM与定位系统，实现复杂停车场高精度感知。
Towards Autonomous Indoor Parking: A Globally Consistent Semantic SLAM System and A Semantic Localization Subsystem
- 用多传感器融合与鸟瞰语义信息构建约束因子图优化位姿与地图
- 在真实数据集上实现稳定全局定位与精确语义建图，优于现有SLAM方法
- 适合自动驾驶泊车、智能导航等场景，尤其对语义理解要求高的应用
本文提出一种全局一致的语义SLAM系统（GCSLAM）和语义融合定位子系统（SF-Loc），可在复杂停车场实现精准语义建图与鲁棒定位。系统采用前视与环视摄像头、IMU及轮速编码器作为输入传感器。GCSLAM引入基于多传感器数据与鸟瞰（BEV）语义信息的语义约束因子图，优化位姿与语义地图，并集成全局车位管理模块，用于存储和管理车位观测结果。SF-Loc则利用GCSLAM构建的语义地图进行基于地图的定位，通过新设计的因子图融合配准结果与里程计位姿。在两个真实世界数据集上的实验表明，本系统在鲁棒全局定位与精确语义映射方面显著优于现有SLAM方法。
原文摘要 · Abstract (English)
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.
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