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Visual Localizer: Outdoor Localization Based on ConvNet Descriptor and Global Optimization for Visually Impaired
Shufei Lin1, Ruiqi Cheng2, Kaiwei Wang3
1State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310027, China. linshufei@zju.edu.cn.
Sensors (Basel, Switzerland)
|August 2, 2018
Summary
This study introduces Visual Localizer, a robust system for assisted navigation. It enhances environmental awareness for visually impaired individuals by improving visual localization accuracy and safety.
Area of Science:
- Computer Vision
- Robotics
- Human-Computer Interaction
Background:
- Assisted navigation systems are crucial for visually impaired individuals.
- Existing visual localization algorithms, often designed for autonomous vehicles, face challenges with wearable cameras due to viewpoint and appearance variations.
- These limitations hinder their direct application in assisted navigation scenarios.
Purpose of the Study:
- To develop a robust visual localization system tailored for assisted navigation.
- To address the challenges posed by viewpoint, appearance, and route changes in wearable camera data.
- To enhance the safety and environmental awareness of visually impaired users.
Main Methods:
- A novel system, Visual Localizer, was developed, integrating a Convolutional Neural Network (ConvNet) descriptor with global optimization.
- Five prominent ConvNets were evaluated, with GoogLeNet selected for its superior environmental invariance.
- Compressed convolutional layers of GoogLeNet were concatenated for efficient image representation (thousands of bytes).
- A network flow model was employed for global optimization to enhance image matching robustness.
Main Results:
- GoogLeNet demonstrated the best performance regarding environmental invariance among the tested ConvNets.
- The proposed system achieved efficient image representation using compressed convolutional layers.
- Global optimization using a network flow model significantly improved the robustness of image matching.
- Experiments with visually impaired volunteers confirmed the system's effectiveness in assisted navigation contexts.
Conclusions:
- The Visual Localizer system offers a robust solution for visual localization in assisted navigation.
- The integration of ConvNet descriptors and global optimization effectively handles variations common in wearable camera data.
- The system shows significant promise for improving the independence and safety of visually impaired individuals.
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