An Improved PDR/UWB Integrated System for Indoor Navigation Applications

Guo, Siyu; Zhang, Yitong; Gui, Xin; Han, Lina

doi:10.1109/jsen.2020.2981635

Cited by 43 publications

(22 citation statements)

References 47 publications

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“…Compared with the traditional front wheel steering mode, the four-wheel steering vehicle 1 has great improvement in handling stability and mobility, which makes the unmanned vehicle adapt to more complex road environment, such as the narrow and complex indoor environments. Dead reckoning algorithm [2][3][4] is a very common positioning method in the current fourwheel steering vehicles autonomous navigation process. In the process of four-wheel steering vehicle positioning, the position and posture of the vehicle estimated by the odometer and electronic compass will result in positioning uncertainty due to the inherent measurement error of the odometer and electronic compass, and this uncertainty will increase over time.…”

Section: Introductionmentioning

confidence: 99%

Combining extended Kalman filtering and rapidly-exploring random tree: An improved autonomous navigation strategy for four-wheel steering vehicle in narrow indoor environments

Liu

Jing

Wan

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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Autonomous navigation in narrow indoor environments such as indoor factory, warehouse and laboratory environments, and so on requires higher flexibility and navigation accuracy of the vehicle. This article presents an autonomous navigation method for four-wheel steering vehicle which combines extended Kalman filtering (EKF) and rapidly-exploring random tree (RRT) to improve the precision and flexibility of autonomous navigation of the vehicle in narrow indoor environments. The four-wheel steering model was established by the key parameters such as shape size and minimum angle of rotation of the experimental vehicle. Considering the problem that the uncertainty of pose estimation increases with time during autonomous navigation, an error model is schemed by adding noise to the output terminal of the analog odometer sensor. In order to suppress the accumulation of the uncertainty and keep it stable for a long time, the prediction and update steps of Kalman filter are introduced to filter the error. Then, the simultaneous positioning and mapping are established. Based on accurate positioning, a set of driving paths to reach the target is generated by RRT sampling algorithm. The simulation results show that positioning uncertainty remains stable over time, which verifies the effectiveness of the method. The overall positioning percentage error is 0.21%. Compared with traditional dead reckoning algorithm, the positioning accuracy is improved by 73.1% and the vehicle flexibility is increased by 68.6%. The four-wheel steering vehicle can find an ideal trajectory in narrow indoor environments, which assures the efficiency of the autonomous navigation and the traveling quality of the navigation route. Finally, the experimental results are consistent with the simulation results, which further verifies the effectiveness of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%

Combining extended Kalman filtering and rapidly-exploring random tree: An improved autonomous navigation strategy for four-wheel steering vehicle in narrow indoor environments

Liu

Jing

Wan

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

show abstract

“…To correctly detect and eliminate UWB NLOS multipath error, machine learning, deep learning, numerical analysis and other methods are used in UWB positioning to give UWB positioning results more robustness [17][18][19][20][21]. Guo et al [22] adaptively adjusted the variance factor by constructing the evaluation factor of NLOS to improve the robustness of the model. Rayavarapu and Mahapatro [23] proposed the bagging-based ensembled classifier to identify and eliminate the impact of NLOS error on positioning results.…”

Section: Introductionmentioning

confidence: 99%

BDS/GPS/UWB Adaptively Robust EKF Tightly Coupled Navigation Model Considering Pedestrian Motion Characteristics

et al. 2022

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In the indoor and outdoor transition area, due to its poor availability in a complex positioning environment, the BDS/GPS SPP (single-point positioning by combining BeiDou Navigation Satellite System (BDS) and Global Positioning System (GPS)) is unable to provide an effective positioning service. In view of the poor positioning accuracy and low sampling rate of the BDS/GPS SPP and the gross error, such as the non-line-of-sight error of UWB (Ultra-Wide-Band), making the accuracy of positioning results poor, a BDS/GPS/UWB tightly coupled navigation model considering pedestrian motion characteristics is proposed to make positioning results more reliable and accurate in the transition area. The core content of this paper is divided into the following three parts: (1) Firstly, the dynamic model and positioning theories of BDS/GPS SPP and UWB are introduced, respectively. (2) Secondly, the BDS/GPS/UWB tightly coupled navigation model is proposed. An environment discrimination factor is introduced to adaptively adjust the variance factor of the system state. At the same time, the gross error detection factor is constructed by using the a posteriori residuals to make the variance factor of the measurement information of the combined positioning system able to be adjusted intelligently for the purpose of eliminating the interference of gross error observations on positioning results. On the other hand, pedestrian motion characteristics are introduced to establish the constraint equation to improve the consistency of positioning accuracy. (3) Thirdly, the actual measured data are used to demonstrate and analyze the reliability of the positioning model proposed by this paper. The experimental results show that the BDS/GPS/UWB tightly coupled navigation model can effectively improve the accuracy and availability of positioning. Compared with BDS/GPS SPP, the accuracy of this model is improved by 57.8%, 76.0% and 56.5% in the E, N and U directions, respectively.

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“…However, the influence of dynamic model errors was not considered in this algorithm. Guo et al constructed a NLOS error recognition method based on the intensity distribution characteristics of the UWB signal, and they realized the combined location of PDR and UWB based on the Kalman filter [ 39 ]. Tong et al realized the combined location of PDR and UWB based on the weighted method, but the best way to determine the weight factor for different scenes remains to be studied [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

Robustly Adaptive EKF PDR/UWB Integrated Navigation Based on Additional Heading Constraint

Yuan

Zhang

Jian

et al. 2021

Sensors

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At present, GNSS (Global Navigation Satellite System) positioning technology is widely used for outdoor positioning services because of its high-precision positioning characteristics. However, in indoor environments, effective position information cannot be provided, because of the signals being obscured. In order to improve the accuracy and continuity of indoor positioning systems, in this paper, we propose a PDR/UWB (Pedestrian Dead Reckoning and Ultra Wide Band) integrated navigation algorithm based on an adaptively robust EKF (Extended Kalman Filter) to address the problem of error accumulation in the PDR algorithm and gross errors in the location results of the UWB in non-line-of-sight scenarios. First, the basic principles of UWB and PDR location algorithms are given. Then, we propose a loose combination of the PDR and UWB algorithms by using the adaptively robust EKF. By using the robust factor to adjust the weight of the observation value to resist the influence of the gross error, and by adjusting the variance of the system adaptively according to the positioning scene, the algorithm can improve the robustness and heading factor of the PDR algorithm, which is constrained by indoor maps. Finally, the effectiveness of the algorithm is verified by the measured data. The experimental results showed that the algorithm can not only reduce the accumulation of PDR errors, but can also resist the influence of gross location errors under non-line-of-sight UWB scenarios.

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An Improved PDR/UWB Integrated System for Indoor Navigation Applications

Cited by 43 publications

References 47 publications

Combining extended Kalman filtering and rapidly-exploring random tree: An improved autonomous navigation strategy for four-wheel steering vehicle in narrow indoor environments

Combining extended Kalman filtering and rapidly-exploring random tree: An improved autonomous navigation strategy for four-wheel steering vehicle in narrow indoor environments

BDS/GPS/UWB Adaptively Robust EKF Tightly Coupled Navigation Model Considering Pedestrian Motion Characteristics

Robustly Adaptive EKF PDR/UWB Integrated Navigation Based on Additional Heading Constraint

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