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A Heterogeneous Sensing System-Based Method for Unmanned Aerial Vehicle Indoor Positioning
Can Wang1, Kang Li2, Guoyuan Liang3
1Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China. can.wang@siat.ac.cn.
Sensors (Basel, Switzerland)
|August 11, 2017
Summary
This study introduces a new indoor positioning system for micro Unmanned Aerial Vehicles (UAVs). By fusing data from structured light, ultra-wideband (UWB), and inertial navigation systems (INS), it achieves higher accuracy for UAV navigation.
Area of Science:
- Robotics
- Navigation Systems
- Sensor Fusion
Background:
- Indoor navigation for micro Unmanned Aerial Vehicles (UAVs) presents significant positioning accuracy challenges.
- Global Positioning System (GPS) is unreliable in indoor environments, necessitating alternative solutions.
- Limited indoor spaces require advanced technologies for precise UAV operation.
Purpose of the Study:
- To propose a novel indoor self-positioning system for micro UAVs.
- To enhance positioning accuracy for UAVs in both motion and hovering states.
- To develop a heterogeneous sensing system integrating multiple sensor modalities.
Main Methods:
- Development of a custom, low-cost structured light scanner integrated with a camera.
- Implementation of adaptive Kalman filtering for fusing Inertial Navigation System (INS) and Ultra-Wideband (UWB) data during UAV movement.
- Application of Gauss filtering for fusing UWB and structured light scanner data during UAV hovering.
Main Results:
- The proposed heterogeneous sensing system significantly improves UAV positioning accuracy.
- Enhanced accuracy was demonstrated in both dynamic (motion) and static (hovering) states.
- The integrated system outperformed single-sensor positioning methods.
Conclusions:
- The novel indoor self-positioning system effectively addresses UAV navigation challenges.
- Heterogeneous sensor fusion provides superior positioning accuracy compared to individual sensors.
- This approach enables reliable and precise indoor navigation for micro UAVs.
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