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PALMS+: Modular Image-Based Floor Plan Localization Leveraging Depth Foundation Model
Yunqian Cheng1, Benjamin Princen1, Roberto Manduchi1
1University of California, Santa Cruz, Santa Cruz, United States.
IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision
|May 11, 2026
Summary
This study introduces PALMS+, an image-based indoor localization system that reconstructs 3D models for accurate positioning without infrastructure. It improves upon existing methods for applications like emergency response and navigation.
Area of Science:
- Computer Vision
- Robotics
- Indoor Navigation
Background:
- Indoor localization in GPS-denied areas is vital for emergency response and assistive navigation.
- Existing vision-based methods like PALMS are limited by smartphone LiDAR range and indoor layout ambiguity.
- Infrastructure-free localization remains a significant challenge in complex indoor environments.
Purpose of the Study:
- To develop PALMS+, a modular, image-based system for accurate indoor localization.
- To overcome the limitations of previous vision-based localization techniques.
- To provide a robust solution for infrastructure-free indoor positioning.
Main Methods:
- Reconstructing scale-aligned 3D point clouds from posed RGB images using a foundation monocular depth estimation model (Depth Pro).
- Employing geometric layout matching by convolving reconstructed point clouds with floor plans.
- Integrating the system with a particle filter for sequential localization.
Main Results:
- PALMS+ demonstrated superior stationary localization accuracy compared to PALMS and F³Loc on Structured3D and custom campus datasets.
- The system achieved lower localization errors in real-world trajectory tests when integrated with a particle filter.
- PALMS+ requires no additional training and shows robustness for camera-free tracking.
Conclusions:
- PALMS+ offers a significant advancement in infrastructure-free indoor localization using monocular vision.
- The system's modularity and performance highlight its potential for diverse indoor navigation applications.
- The proposed method provides a robust and accurate solution for indoor positioning challenges.
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