Experimental Assessment of UWB and Vision-Based Car Cooperative Positioning System

Abstract

The availability of global navigation satellite systems (GNSS) on consumer devices has caused a dramatic change in every-day life and human behaviour globally. Although GNSS generally performs well outdoors, unavailability, intentional and unintentional threats, and reliability issues still remain. This has motivated the deployment of other complementary sensors in such a way that enables reliable positioning, even in GNSS-challenged environments. Besides sensor integration on a single platform to remedy the lack of GNSS, data sharing between platforms, such as in collaborative positioning, offers further performance improvements for positioning. An essential element of this approach is the availability of internode measurements, which brings in the strength of a geometric network. There are many sensors that can support ranging between platforms, such as LiDAR, camera, radar, and many RF technologies, including UWB, LoRA, 5G, etc. In this paper, to demonstrate the potential of the collaborative positioning technique, we use ultra-wide band (UWB) transceivers and vision data to compensate for the unavailability of GNSS in a terrestrial vehicle urban scenario. In particular, a cooperative positioning approach exploiting both vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) UWB measurements have been developed and tested in an experiment involving four cars. The results show that UWB ranging can be effectively used to determine distances between vehicles (at sub-meter level), and their relative positions, especially when vision data or a sufficient number of V2V ranges are available. The presence of NLOS observations is one of the principal factors causing a decrease in the UWB ranging performance, but modern machine learning tools have shown to be effective in partially eliminating NLOS observations. According to the obtained results, UWB V2I can achieve sub-meter level of accuracy in 2D positioning when GNSS is not available. Combining UWB V2I and GNSS as well V2V ranging may lead to similar results in cooperative positioning. Absolute cooperative positioning of a group of vehicles requires stable V2V ranging and that a certain number of vehicles in the group are provided with V2I ranging data. Results show that meter-level accuracy is achieved when at least two vehicles in the network have V2I data or reliable GNSS measurements, and usually when vehicles lack V2I data but receive V2V ranging to 2–3 vehicles. These working conditions typically ensure the robustness of the solution against undefined rotations. The integration of UWB with vision led to relative positioning results at sub-meter level of accuracy, an improvement of the absolute positioning cooperative results, and a reduction in the number of vehicles required to be provided with V2I or GNSS data to one.

On the Estimation of Vehicle Trajectories With a Mini-Uas

“…Then, as long as a vehicle point is identified on the UAS imagery, for instance by determining the vehicle region by means of background subtraction or by using a deep-learning based method (Masiero et al, 2021b), its 3D position can be estimated via forward ray intersection (assuming the vehicle height approximately known, and that a digital terrain model of the area is available, e.g. obtained via photogrammetric reconstruction using the UAS imagery).…”

A Comparison Between Uwb and Laser-Based Pedestrian Tracking

“…UWB-based positioning has already been recently investigated by certain of the authors: the reader is referred for instance to (Masiero et al, 2021) for a detailed description of the UWB positioning algorithm used here, in a cooperative approach fashion.…”

Range-Based Network Formation to Support Georeferencing of Mapping Swarm Platforms

“…Collaborative navigation or cooperative positioning is a recent concept that is based on data sharing between navigation platforms, such as a mapping swarm (Masiero et al, 2021). If platforms share their navigation sensor data, such as GNSS and IMU as well as range and angular observations between platforms, then a central navigation filter can compute an optimal solution for the entire swarm.…”