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An Adaptive Threshold-Based Pixel Point Tracking Algorithm Using Reference Features Leveraging the Multi-State
Zohaib Wahab Memon1, Yu Chen1, Hai Zhang1
1School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.
Sensors (Basel, Switzerland)
|May 14, 2025
Summary
This study introduces a novel visual-inertial odometry method that tracks featureless pixels to create detailed depth maps, improving localization and environmental perception for robots.
Area of Science:
- Robotics
- Computer Vision
- Sensor Fusion
Background:
- Monocular visual-inertial odometry (VIO) using the MSCKF algorithm is efficient for localization.
- Traditional VIO struggles with textureless environments due to insufficient feature points for depth estimation.
Purpose of the Study:
- To enhance depth map generation in VIO systems, especially in textureless environments.
- To improve the robustness and detail of depth estimation for robotic applications.
Main Methods:
- Proposed a novel algorithm to extract and track arbitrary featureless pixel points alongside traditional features.
- Utilized optical flow and geometric constraints from adjacent images for robust point tracking.
- Segmented images into grids for targeted pixel point extraction and depth estimation.
Main Results:
- Achieved more detailed depth maps compared to traditional feature point-based methods.
- Successfully tracked featureless pixels in textureless regions, overcoming a key limitation.
- Demonstrated real-time depth map generation within the OpenVINS framework.
Conclusions:
- The proposed method significantly improves depth estimation accuracy and detail in VIO systems.
- Enables enhanced environmental perception for applications like obstacle detection and path planning.
- Offers a robust solution for VIO in challenging, texture-limited environments.
