Cooperative localization of vehicles sharing GNSS pseudoranges corrections with no base station using set inversion

Abstract

Fully distributed localization methods with no central server are relevant for autonomous vehicles that need real-time cooperation. In this paper, mobile vehicles share estimates of GNSS pseudoranges common errors also known as biases. The biases that affect the pseudoranges are mainly due to signal propagation and inaccurate ephemeris data. By describing the measurements models as geometric constraints on intervals, cooperative localization turns into distributed set inversion problem. The solution of this problem is guaranteed to contain the true vehicles positions. We consider vehicles which cooperate and exchange information in order to improve the absolute and relative estimation by fusing pseudoranges corrections shared between them. Results using real measurements are presented to illustrate the performance of the proposed approach in comparison with a standalone method.

Cooperative Localization for Autonomous Vehicles Sharing GNSS Measurements

“…Other results illustrating more in details the behavior of the proposed C-SIVIAP method can be found in [40].…”

An accurate cooperative positioning system for vehicular safety applications

“…On the other hand, CooPS depends on a GNSS system and then, whenever it fails, e.g., when the vehicle enters a canyon, tunnel, or dense forest, the navigation may face interruptions. In this case, dead reckoning positioning techniques [28,29] can be used. Dead reckoning techniques do 165 not require additional sensors and can operate using only data from available built-in sensors, such as wheel speed or steering angle sensors.…”

Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles

“…With this method, the distance between the vehicles can be estimated with less than 0.80 $\mathrm{m}$ and 1.30 $\mathrm{m}$ accuracy in highway and urban environments, respectively. A technique in which the pseudorange biases are estimated in a cooperative way between the vehicles is presented by Lassoued et al in [96]. A horizontal relative positioning error of 2.46 $\mathrm{m}$ is achieved.…”