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Related Experiment Video

Updated: May 12, 2026

Photorealistic Learned Landscapes for Augmented Reality
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Published on: June 27, 2025

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
PubMed
Summary
This summary is machine-generated.

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.

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Published on: December 1, 2016

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.