用空间关系提升文本与点云的定位精度，助力机器人自主导航。
SpatiaLoc: Leveraging Multi-Level Spatial Enhanced Descriptors for Cross-Modal Localization
- 分阶段建模物体间空间关系，从实例到全局
- 在KITTI360Pose上定位误差比顶尖方法降低12.3%
- 适合做多模态定位、机器人导航的研究者
基于文本与点云的跨模态定位使机器人能够通过自然语言描述实现自我定位，在自动驾驶和人机交互中有重要应用。由于物体在文本与点云中常重复出现，空间关系成为最关键的定位线索。为此，我们提出SpatiaLoc框架，采用粗到精策略，强调实例级与全局级的空间关系建模。粗粒度阶段引入二次贝塞尔曲线建模实例级空间关系的贝塞尔增强对象空间编码器（BEOSE），并使用频域感知编码器（FAE）在全局层面生成频率域空间表示。细粒度阶段设计了不确定性感知高斯精细定位器（UGFL），通过将预测建模为高斯分布，并使用具有不确定性感知的损失函数回归2D位置。在KITTI360Pose上的大量实验表明，SpatiaLoc显著优于现有最先进方法。
原文摘要 · Abstract (English)
Cross-modal localization using text and point clouds enables robots to localize themselves via natural language descriptions, with applications in autonomous navigation and interaction between humans and robots. In this task, objects often recur across text and point clouds, making spatial relationships the most discriminative cues for localization. Given this characteristic, we present SpatiaLoc, a framework utilizing a coarse-to-fine strategy that emphasizes spatial relationships at both the instance and global levels. In the coarse stage, we introduce a Bezier Enhanced Object Spatial Encoder (BEOSE) that models spatial relationships at the instance level using quadratic Bezier curves. Additionally, a Frequency Aware Encoder (FAE) generates spatial representations in the frequency domain at the global level. In the fine stage, an Uncertainty Aware Gaussian Fine Localizer (UGFL) regresses 2D positions by modeling predictions as Gaussian distributions with a loss function aware of uncertainty. Extensive experiments on KITTI360Pose demonstrate that SpatiaLoc significantly outperforms existing state-of-the-art (SOTA) methods.
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