Abstract
Among the existing wireless indoor positioning systems, UWB (ultra-wideband) is one of the most promising solutions. However, the single UWB positioning system is affected by factors such as non-line of sight and multipath, and the navigation accuracy will decrease. In order to make up for the shortcomings of a single UWB positioning system, this paper proposes a scheme based on binocular VO (visual odometer) and UWB sensor fusion. In this paper, the original distance measurement data of UWB and the position information of binocular VO are merged by adaptive Kalman filter, and the structural design of the fusion system and the realization of the fusion algorithm are elaborated. The experimental results show that compared with a single positioning system, the proposed data fusion method can significantly improve the positioning accuracy.
Highlights
In an indoor environment, obtaining the location and orientation of a moving vehicle is an important part of the autonomous navigation of indoor vehicles
The inertial navigation system (INS) can provide positioning information for the vehicle both indoors and outdoors, but its positioning accuracy is directly proportional to the manufacturing cost
For the inertial navigation system used in the vehicle, its positioning error will accumulate over time, and it cannot provide long-term reliable positioning for the vehicle [2]
Summary
In an indoor environment, obtaining the location and orientation of a moving vehicle is an important part of the autonomous navigation of indoor vehicles. In some occasions where high indoor positioning accuracy is required, a high-precision optical motion capture system [1] can be selected, which can provide centimeter-level positioning accuracy. It is costly and complicated in structure, and has high requirements for layout and installation in an indoor scene, so it cannot be widely used in the field of vehicle interior positioning. For the inertial navigation system used in the vehicle, its positioning error will accumulate over time, and it cannot provide long-term reliable positioning for the vehicle [2]. Similar to the IMU (inertial measurement unit), its positioning error will drift, which is not suitable for long-term positioning navigation [3]
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