Automatic Odometry-Less OpenDRIVE Generation From Sparse Point Clouds

TL;DR

This paper introduces a novel method to generate realistic road maps in OpenDRIVE format directly from sparse point clouds, eliminating the need for odometry, sensor fusion, or machine learning, thereby enhancing simulation-based testing for automated driving.

Contribution

It presents a new approach for creating OpenDRIVE road representations solely from sparse point clouds, independent of odometry and multi-sensor data, simplifying the process for simulation purposes.

Findings

Successfully generates OpenDRIVE maps from sparse point clouds.

Independent of sensor mounting position and odometry data.

Suitable for realistic simulation and testing of automated driving.

Abstract

High-resolution road representations are a key factor for the success of (highly) automated driving functions. These representations, for example, high-definition (HD) maps, contain accurate information on a multitude of factors, among others: road geometry, lane information, and traffic signs. Through the growing complexity and functionality of automated driving functions, also the requirements on testing and evaluation grow continuously. This leads to an increasing interest in virtual test drives for evaluation purposes. As roads play a crucial role in traffic flow, accurate real-world representations are needed, especially when deriving realistic driving behavior data. This paper proposes a novel approach to generate realistic road representations based solely on point cloud information, independent of the LiDAR sensor, mounting position, and without the need for odometry data, multi-sensor fusion, machine learning, or highly-accurate calibration. As the primary use case is simulation, we use the OpenDRIVE format for evaluation.