High Precision Outdoor and Indoor Reference State Estimation for Testing Autonomous Vehicles.

Eduardo Sánchez Morales,Bertold Huber,Andrés García Higuera,Michael Botsch,Julian Dauth

doi:10.3390/s21041131

Eduardo Sánchez Morales, Bertold Huber + Show 3 more

Open Access

https://doi.org/10.3390/s21041131

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Abstract

A current trend in automotive research is autonomous driving. For the proper testing and validation of automated driving functions a reference vehicle state is required. Global Navigation Satellite Systems (GNSS) are useful in the automation of the vehicles because of their practicality and accuracy. However, there are situations where the satellite signal is absent or unusable. This research work presents a methodology that addresses those situations, thus largely reducing the dependency of Inertial Navigation Systems (INSs) on the SatNav. The proposed methodology includes (1) a standstill recognition based on machine learning, (2) a detailed mathematical description of the horizontation of inertial measurements, (3) sensor fusion by means of statistical filtering, (4) an outlier detection for correction data, (5) a drift detector, and (6) a novel LiDAR-based Positioning Method (LbPM) for indoor navigation. The robustness and accuracy of the methodology are validated with a state-of-the-art INS with Real-Time Kinematic (RTK) correction data. The results obtained show a great improvement in the accuracy of vehicle state estimation under adverse driving conditions, such as when the correction data is corrupted, when there are extended periods with no correction data and in the case of drifting. The proposed LbPM method achieves an accuracy closely resembling that of a system with RTK.

Highlights

Autonomous driving has become a popular trend in automotive research
Given the extensive scientific community working on these systems, their practicality, and especially their high accuracy, Satellite Navigation (SatNav) receivers have become the most common choice of sensor to fuse with Inertial Measurement Units (IMU)
The performance of the standstill recognition is measured in terms of its classification performance according to Table 1, as well as its robustness against false positives

Summary

Introduction

Autonomous driving has become a popular trend in automotive research. The motivation for autonomous driving ranges from comfort or practicality functions to safety critical applications. Autonomous driving functions need to be subjected to an extensive process of testing and validation which requires a highly accurate reference vehicle state. A common practice to generate such a highly accurate reference vehicle state is the fusion of data from Inertial Measurement Units (IMU) with measurements from external sensors. Given the extensive scientific community working on these systems, their practicality, and especially their high accuracy, Satellite Navigation (SatNav) receivers have become the most common choice of sensor to fuse with IMUs. Even consumer-grade receivers are capable of acquiring information from various GNSS (such as GPS, GLONASS, Galileo, Beidou, etc.), which enables the receivers to accurately estimate several state variables

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