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Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review
Mengshen Yang1,2,3, Xu Sun1,4, Fuhua Jia1
1Department of Mechanical, Materials and Manufacturing Engineering, The Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China.
Polymers
|May 28, 2022
Summary
Indoor localization using robots is challenging due to signal obstruction. This review covers sensor modalities like IMUs, LiDAR, radar, and cameras for robot odometry, enhancing indoor navigation capabilities.
Area of Science:
- Robotics and Sensor Technology
- Indoor Navigation Systems
- Polymer Science in Sensors
Background:
- Global Navigation Satellite Systems (GNSSs) offer limited accuracy for indoor localization due to signal obstruction.
- Self-contained localization schemes are crucial for reliable indoor navigation of moving robots.
- Modern robots utilize advanced sensors and algorithms for environmental perception and localization.
Purpose of the Study:
- To provide a comprehensive review of sensor modalities for indoor odometry.
- To analyze algorithms and fusion frameworks for robot pose estimation and odometry.
- To outline the principles and applications of indoor odometry for robotic systems.
Main Methods:
- Review of Inertial Measurement Units (IMUs), Light Detection and Ranging (LiDAR), radar, and cameras for indoor odometry.
- Exploration of polymer applications within these sensor technologies.
- Analysis of algorithms and sensor fusion frameworks for pose estimation.
Main Results:
- Detailed examination of various sensor modalities and their suitability for indoor odometry.
- Discussion on the integration of different sensors and algorithms for enhanced localization accuracy.
- Identification of polymer applications contributing to sensor performance in indoor environments.
Conclusions:
- Indoor odometry is a viable alternative to GNSS for robotic localization.
- Sensor fusion and advanced algorithms are key to achieving robust indoor pose estimation.
- This review provides a foundational understanding and future outlook for indoor odometry research.
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