UWB/Binocular VO Fusion Algorithm Based on Adaptive Kalman Filter

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.

Self-Corrective Sensor Fusion for Drone Positioning in Indoor Facilities

“…Even though there is a clear trade-off between positioning accuracy and number of visual odometer restarts (the higher the first the more restarts are necessary), our initial accuracy goal (∼5 cm) was fulfilled. We have also demonstrated that, in this scenario, our approach keeps the benefits of visual odometry by correcting it with UWB data, outperforming the Kalman fusion in [25] in case visual odometry fails, both in terms of stopping point accuracy and path RMSE.…”

“…Direct CTRA Kalman fusion performed better than CTRA Kalman fusion based on linear combinations of sensor readings as in [25] if the RealSense unit worked correctly. Also in that case, the results of these two variants were superior to CTRA Kalman-filtered Pozyx data, reflecting the beneficial effect of RealSense data.…”

“…The results of the experiments were consistent with our assumptions about the characteristics of indoor facilities. The challenges they pose to positioning technologies are less evident in more open scenarios like the experimental setting in [25]. In the case of UWB positioning (Pozyx in our trials), indoor obstacles such as reflective surfaces or equipment nearby cause noisy readings.…”

“…We deployed in our lab a Pozyx positioning system with four anchors B1 -B4 in a square arrangement as shown in Figure 3, all of them at a height of 1 m. The Pozyx unit provided absolute localization data based on trilateration in a coordinate system, as described in [25]. It is possible to improve its accuracy by combining multilateration with anchor selection to overcome measurement errors in extreme environments [31], but this was not our case, so we employed the default algorithm settings.…”

UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis

“…The findings from the study indicated that this method had a higher positioning accuracy compared with that of the traditional federated EKF method. A sensor fusion scheme based on binocular visual odometer (VO)/UWB was previously reported (Zeng et al, 2019 ). In the study, the adaptive Kalman filtering method was used to fuse the UWB raw distance measurement data and the binocular VO position information.…”