Lane-level navigation system for ground vehicles with lidar and cellular signals

Abstract

A vehicular simultaneous localization and mapping may fuse lidar data and pseudoranges extracted from ambient cellular LTE towers. An ICP algorithm may be used to extract odometry measurements from successive lidar scans. A robust and computationally efficient feature extraction method may be used to detect edge lines and feature points from the lidars point cloud. Then, a point registration technique using a maximum likelihood approach allows the estimation of the covariance of the odometry error, which is used in EKF propagation. The proposed approach consists of a mapping mode when GNSS signals are available and subsequently a SLAM mode when GNSS signals become unavailable. The cellular transmitters states, namely position and clock bias and clock drift, are continuously estimated in both modes. Simulation and experimental results validate the accuracy of these systems and methods, and provides lane-level localization without GNSS signals.