Abstract
The indoor autonomous navigation of unmanned aerial vehicles (UAVs) is the current research hotspot. Unlike the outdoor broad environment, the indoor environment is unknown and complicated. Global Navigation Satellite System (GNSS) signals are easily blocked and reflected because of complex indoor spatial features, which make it impossible to achieve positioning and navigation indoors relying on GNSS. This article proposes a set of indoor corridor environment positioning methods based on the integration of WiFi and IMU. The zone partition-based Weighted K Nearest Neighbors (WKNN) algorithm is used to achieve higher WiFi-based positioning accuracy. On the basis of the Error-State Kalman Filter (ESKF) algorithm, WiFi-based and IMU-based methods are fused together and realize higher positioning accuracy. The probability-based optimization method is used for further accuracy improvement. After data fusion, the positioning accuracy increased by 51.09% compared to the IMU-based algorithm and by 66.16% compared to the WiFi-based algorithm. After optimization, the positioning accuracy increased by 20.9% compared to the ESKF-based data fusion algorithm. All of the above results prove that methods based on WiFi and IMU (low-cost sensors) are very capable of obtaining high indoor positioning accuracy.
Highlights
Academic Editor: ArturoAn unmanned aerial vehicle (UAV) is a drone that integrates various sensors, flight control systems, data processing systems, power systems, and other modules, which can autonomously complete specified tasks without human intervention
The indoor aircraft platform is independently designed to conduct a series of expercombined positioning method, the for InInthis combined positioning method,and and theconstrained constrainedESKF
Due to its light weight, small size, and flexible motion, indoor drones can adapt to narrow spaces, and are of great significance for completing tasks, such as indoor reconnaissance, indoor rescue, and target pickup
Summary
Academic Editor: ArturoAn unmanned aerial vehicle (UAV) is a drone that integrates various sensors, flight control systems, data processing systems, power systems, and other modules, which can autonomously complete specified tasks without human intervention. Rotor-wing UAVs, especially quadcopters, have good development prospects in indoor environments, due to their compact structure, easy hovering, and convenient side-flight. Because of topological structures and spatial features, the indoor environment is complex, and signals are blocked and reflected. Quadcopters cannot rely on GNSS to realize positioning and navigation indoors. Pseudolite-based methods can solve GNSS-based method problems in an indoor environment [1,2]. Considering that current indoor environments can receive external source signals, such as WiFi and Bluetooth, deploying pseudolite transmitters costs a lot, and method-based pseudolites may face near–far problems and time synchronization problems [1], this article mainly discusses other indoor positioning methods
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