用手机摄像头+深度模型，实现无基建室内精确定位
PALMS+: Modular Image-Based Floor Plan Localization Leveraging Depth Foundation Model
- 通过单目深度模型重建带尺度的3D点云，结合平面布局匹配定位
- 在4栋校园建筑上测试，静态定位精度优于现有方法，无需训练
- 可与粒子滤波结合，实现连续追踪，适合无基础设施场景
在无GPS环境下的室内定位对应急响应和辅助导航至关重要。基于视觉的方法如PALMS仅需平面图和静止扫描即可实现无基础设施定位，但受限于手机LiDAR测距短和室内布局模糊。本文提出PALMS+，一种模块化图像基系统，利用深度基础模型Depth Pro从带姿态的RGB图像中重建尺度对齐的3D点云，并通过卷积与平面图进行几何布局匹配，输出位置与朝向的后验分布，可用于直接或序列化定位。在Structured3D及包含80个观测值的自建校园数据集（4栋大型建筑）上评估，PALMS+在静态定位精度上优于PALMS和F3Loc，且无需训练。进一步在33条真实轨迹上集成粒子滤波进行序列化定位，其误差更低，验证了其在无相机追踪中的鲁棒性及无基础设施应用潜力。代码与数据已公开。
原文摘要 · Abstract (English)
Indoor localization in GPS-denied environments is crucial for applications like emergency response and assistive navigation. Vision-based methods such as PALMS enable infrastructure-free localization using only a floor plan and a stationary scan, but are limited by the short range of smartphone LiDAR and ambiguity in indoor layouts. We propose PALMS$+$, a modular, image-based system that addresses these challenges by reconstructing scale-aligned 3D point clouds from posed RGB images using a foundation monocular depth estimation model (Depth Pro), followed by geometric layout matching via convolution with the floor plan. PALMS$+$ outputs a posterior over the location and orientation, usable for direct or sequential localization. Evaluated on the Structured3D and a custom campus dataset consisting of 80 observations across four large campus buildings, PALMS$+$ outperforms PALMS and F3Loc in stationary localization accuracy -- without requiring any training. Furthermore, when integrated with a particle filter for sequential localization on 33 real-world trajectories, PALMS$+$ achieved lower localization errors compared to other methods, demonstrating robustness for camera-free tracking and its potential for infrastructure-free applications. Code and data are available at https://github.com/Head-inthe-Cloud/PALMS-Plane-based-Accessible-Indoor-Localization-Using-Mobile-Smartphones
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