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A Robust Indoor Localization System Integrating Visual Localization Aided by CNN-Based Image Retrieval with Monte
Song Xu1, Wusheng Chou2,3, Hongyi Dong4
1School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China. keithxs@buaa.edu.cn.
Sensors (Basel, Switzerland)
|January 13, 2019
Summary
This study introduces a new multi-sensor indoor localization system using Convolutional Neural Networks (CNN) and adaptive Monte Carlo localization. It improves accuracy, speed, and robot kidnapping recovery for mobile robots.
Area of Science:
- Robotics
- Computer Vision
- Artificial Intelligence
Background:
- Accurate indoor global localization is crucial for mobile robot navigation.
- Existing methods often struggle with robustness and recovery from localization failures.
Purpose of the Study:
- To propose a novel multi-sensor fusion system for robust indoor global localization.
- To enhance localization accuracy, speed, and the ability to recover from kidnapping events.
Main Methods:
- Integrating Convolutional Neural Network (CNN)-based visual localization with probabilistic localization.
- Employing coarse place recognition via image retrieval and fine localization using adaptive Monte Carlo localization (AMCL).
- Implementing a closed-loop mechanism with Long-Short Term Memory (LSTM) networks for re-localization from kidnapping.
Main Results:
- The proposed system significantly improves localization accuracy and speed compared to conventional methods.
- Demonstrated effective recovery from robot kidnapping scenarios.
- Validated through extensive experimental evaluations.
Conclusions:
- The multi-sensor fusion approach offers a robust solution for indoor global localization.
- The integration of CNN, AMCL, and LSTM enhances system performance and resilience.
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