Precise Positioning of Autonomous Vehicles Combining UWB Ranging Estimations with On-Board Sensors

Abstract

In this paper, we analyze the performance of a positioning system based on the fusion of Ultra-Wideband (UWB) ranging estimates together with odometry and inertial data from the vehicle. For carrying out this data fusion, an Extended Kalman Filter (EKF) has been used. Furthermore, a post-processing algorithm has been designed to remove the Non Line-Of-Sight (NLOS) UWB ranging estimates to further improve the accuracy of the proposed solution. This solution has been tested using both a simulated environment and a real environment. This research work is in the scope of the PRoPART European Project. The different real tests have been performed on the AstaZero proving ground using a Radio Control car (RC car) developed by RISE (Research Institutes of Sweden) as testing platform. Thus, a real time positioning solution has been achieved complying with the accuracy requirements for the PRoPART use case.

A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges

“…The proposed solution utilised the inertial and odometry data of the vehicle along with the UWB ranging estimates to estimate the vehicle position. The estimates obtained were accurate and 95% of the computed positions were shown to have a positioning distance error of less than 27.1 cm [155]. In [156], machine learning was employed to improve the RMSE of UWB based ranging estimates, and the positioning distance error was reduced to less than 10 cm.…”

“…Vehicle Localisation: The GPS used in the vehicles can easily determine the position of the vehicle but in the dense urban environment its localisation becomes more challenging. However, these short-range technologies, especially UWB, can assist localisation in dense environments as they do not require the Line of Sight (LoS) and can easily penetrate in the obstacles [155]. UWB can be regarded as the technology of choice for range-based localisation especially for the vehicles moving in dense clustered environments [157].…”

Visualizing Street Pavement Anomalies through Fog Computing V2I Networks and Machine Learning

“…One example of application in short-range ultra-wideband (UWB) technologies provides estimated ranges to track the vehicle's position in an outdoor environment. Martin et al built an accurate and reliable positioning solution based on the combination of UWB varying estimates and inertial and odometry data of the vehicle [16]. As it has a low cost and a long battery life, ZigBee technology Vehicle Identification is possible.…”

Switching Extended Kalman Filter Bank for Indoor Localization Using Wireless Sensor Networks

“…To exploit stochastic filters, a localization system should be modeled in a state space form that comprises motion and measurement models. Because wireless measurements of WSNs are typically represented by nonlinear measurement models, nonlinear stochastic filters such as the extended Kalman filter (EKF) and the particle filter (PF) are often used for indoor localization [8][9][10][11]. In this study, the EKF was used for indoor localization because it has an advantage over the PF in terms of real-time processing.…”