用高斯语义场提升单次激光雷达定位精度，解决地标重复干扰问题。
Gaussian Semantic Field for One-shot LiDAR Global Localization
- 构建三层场景图，中间层用高斯函数建模连续语义分布。
- 在ScanNet和Oxford RobotCar上实现优于现有方法的定位精度。
- 适合需要快速、精准定位的自动驾驶与机器人应用。
我们提出一种基于轻量级三层场景图的一次性激光雷达全局定位算法，具备语义消歧能力。相较于仅依赖几何信息的方法，基于地标语义匹配的方法虽表现更优，但地标重复易导致对应关系错误。为此，我们通过大量高斯过程学习连续语义函数来建模语义分布，相比离散语义标签，能捕捉更细粒度的地理语义信息，并提供更丰富的度量信息以支持匹配。将该连续函数作为对象层与度量-语义层之间的中间层，构成三层次3D场景图，作为轻量高效的一次性定位后端。我们称该定位流程为Outram-GSF（高斯语义场），并在多个公开数据集上进行了广泛实验，验证其性能优于当前最先进方法。
原文摘要 · Abstract (English)
We present a one-shot LiDAR global localization algorithm featuring semantic disambiguation ability based on a lightweight tri-layered scene graph. While landmark semantic registration-based methods have shown promising performance improvements in global localization compared with geometric-only methods, landmarks can be repetitive and misleading for correspondence establishment. We propose to mitigate this problem by modeling semantic distributions with continuous functions learned from a population of Gaussian processes. Compared with discrete semantic labels, the continuous functions capture finer-grained geo-semantic information and also provide more detailed metric information for correspondence establishment. We insert this continuous function as the middle layer between the object layer and the metric-semantic layer, forming a tri-layered 3D scene graph, serving as a light-weight yet performant backend for one-shot localization. We term our global localization pipeline Outram-GSF (Gaussian semantic field) and conduct a wide range of experiments on publicly available data sets, validating the superior performance against the current state-of-the-art.
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