Enhancing Indoor Localization using ARKit and RANSAC

Introduction

Indoor localization is a crucial technology for navigation where GPS systems fall short, such as in large buildings or complex indoor environments. This blog post explores a novel approach that proposes use of ARKit and advanced image processing algorithms to accurately detect walls and planes, ensuring precise localization. This technology is particularly beneficial for applications like assisting visually impaired individuals, guiding emergency responders, or navigating industrial and commercial environments.

Methodology

The approach involves three primary steps:

1. Prior Estimation and Filtering:

   • Coarse Estimation: We begin by using the last known GPS position to approximate the user's location within the building.

   • Particle Filtering: We apply particle filtering methods, which eliminate impossible locations by analyzing the user’s movement and correlating it with the building's floor plan.

2. ARKit-based Plane Detection:

   • ARKit utilizes Visual-Inertial Odometry (VIO) and iPhone’s LiDAR capabilities to create a precise representation of the environment. Experiments involved detecting vertical planes across various settings, including environments with furniture and architectural complexities.

   • LiDAR Capabilities: In tests, iPhones equipped with LiDAR sensors achieved consistent detection up to 5 meters. Without LiDAR, the detection range decreased, emphasizing the importance of translational movement for accurate results.

3. Advanced Image Processing and Plane Matching:

   • We employed RANSAC (Random Sample Consensus) to match detected planes with a floor plan. The algorithm uses sampled points from both the detected planes and the floor plan to find the best fit, accurately determining the user’s location even in dynamic environments.

Results

• Increased Accuracy: The integration of ARKit and LiDAR significantly extended detection range and accuracy, allowing for precise plane detection in medium to large spaces.

  
  

  

  iPhone with LiDAR provides better distance calculation

  

  Plane wall extraction using ARKit Tool

  • Plane Matching Success: Once the Plane walls are extracted using ARKit tool, the RANSAC algorithm is used to match the detected planes with floor plan features, such as entrances and corridors, validating our approach.

  • Experimental Insights: Planes obstructed by more than 20% of their surface (e.g., due to furniture) posed detection challenges. Variations in plane texture and color also impacted detection accuracy.

  
  

  Applications

  Successful plane detection techniques have tremendous potential across various domains:

  • Assistive Navigation: The technology can be integrated into AR applications for visually impaired users, providing real-time navigational assistance.

  • Emergency Response: Emergency teams can benefit from the system’s precise indoor localization to locate individuals or critical equipment in emergency situations.

  • Industrial and Commercial Uses: Companies can utilize this technology to monitor equipment and personnel within complex indoor environments, enhancing efficiency and safety.

  Future Work

  Moving forward, we aim to refine our algorithms further:

  • Enhanced Machine Learning Integration: We plan to incorporate reinforcement learning models to improve real-time decision-making and navigation.

  • Optimization of Plane Matching Algorithms: We will refine the RANSAC algorithm to handle more complex floor plans and enhance detection accuracy in environments with a high degree of visual noise.

  Conclusion

  This research showcases a robust and accessible method for indoor localization using ARKit and LiDAR-equipped iPhones. By combining advanced algorithms like RANSAC and leveraging the powerful ARKit framework, we have developed an effective system that significantly improves localization accuracy without the need for pre-mapping processes.

  Interested to Know more : Please contact srijon@cogniz.org