Robust Vehicular Localization and Map Matching in Urban Environments Through IMU, GNSS, and Cellular Signals

A framework for ground vehicle localization that uses cellular signals of opportunity (SOPs), a digital map, an inertial measurement unit (IMU), and a Global Navigation Satellite System (GNSS) receiver is developed. This framework aims to enable localization in an urban environment where GNSS signals could be unusable or unreliable. The proposed framework employs an extended Kalman filter (EKF) to fuse pseudorange observables extracted from cellular SOPs, IMU measurements, and GNSS-derived position estimates (when available). The EKF is coupled with a map-matching approach. The framework assumes the positions of the cellular towers to be known, and it estimates the vehicle's states (position, velocity, orientation, and IMU biases) along with the difference between the vehicle-mounted receiver clock error states (bias and drift) and each cellular SOP clock error state. The proposed framework is evaluated experimentally on a ground vehicle navigating in a deep urban area with a limited sky view. Results show a position root-mean-square error (RMSE) of 2.8 m across a 1,380-m trajectory when GNSS signals are available and an RMSE of 3.12 m across the same trajectory when GNSS signals are unavailable for 330 m. Moreover, compared to localization with a loosely coupled GNSS?IMU integrated system, a 22% reduction in the localization error is obtained whenever GNSS signals are available, and an 81% reduction in the localization error is obtained whenever GNSS signals are unavailable.

Localization technologies for navigation and ground vehicle autonomy levels have been evolving hand in hand. Ten years ago, ground vehicle localization systems for navigation consisted of a GPS receiver, a wheel odometer, and an inertial measurement unit (IMU). Localization errors greater than lane-level and periodic dropouts of the navigation solution were tolerable to the driver, who had to follow the path drawn on the GPS navigation system. Although localization and some form of path planning from a start location to a desired destination were performed autonomously, the driver had to steer the car, control acceleration, avoid obstacles, change lanes, and so on. Today, as ground vehicles evolve by incorporating autonomous-type driving technologies (e.g. cruise control, active steering, collision avoidance, lane detection, and so on), the requirements for localization and navigation technologies become more stringent, necessitating the need for additional sensors (lidar, vision, radar, among others). Large errors become less acceptable, and the consistent availability of the navigation solution is critical.