Leveraging Dynamic Objects for Relative Localization Correction in a Connected Autonomous Vehicle Network

TL;DR

This paper introduces a RANSAC-based method that uses both static and dynamic objects for real-time correction of relative localization errors between connected autonomous vehicles, enhancing data fusion accuracy.

Contribution

The novel approach leverages dynamic objects alongside static features for improved relative localization correction in CAV networks, enabling more accurate and efficient data fusion.

Findings

Reduces relative localization error to less than 20 cm.

Effective in real-time scenarios with sufficient dynamic objects.

Highly efficient runtime suitable for autonomous driving.

Abstract

High-accurate localization is crucial for the safety and reliability of autonomous driving, especially for the information fusion of collective perception that aims to further improve road safety by sharing information in a communication network of ConnectedAutonomous Vehicles (CAV). In this scenario, small localization errors can impose additional difficulty on fusing the information from different CAVs. In this paper, we propose a RANSAC-based (RANdom SAmple Consensus) method to correct the relative localization errors between two CAVs in order to ease the information fusion among the CAVs. Different from previous LiDAR-based localization algorithms that only take the static environmental information into consideration, this method also leverages the dynamic objects for localization thanks to the real-time data sharing between CAVs. Specifically, in addition to the static objects like poles, fences, and facades, the object centers of the detected dynamic vehicles are also used as keypoints for the matching of two point sets. The experiments on the synthetic dataset COMAP show that the proposed method can greatly decrease the relative localization error between two CAVs to less than 20cmas far as there are enough vehicles and poles are correctly detected by bothCAVs. Besides, our proposed method is also highly efficient in runtime and can be used in real-time scenarios of autonomous driving.