DA-LMR: A Robust Lane Marking Representation for Data Association

TL;DR

This paper introduces DA-LMR, a detailed lane marking data representation, and DC-SAC, a fast data association method, significantly improving localization accuracy in noisy scenarios.

Contribution

It proposes a novel geometric lane marking representation and a heuristic data association method, enhancing robustness and efficiency in localization tasks.

Findings

Achieved 98.1% precision and 99.7% recall in noisy data scenarios.

Outperformed state-of-the-art data representation and association methods.

Demonstrated robustness with 0.5 m standard deviation noise.

Abstract

While complete localization approaches are widely studied in the literature, their data association and data representation subprocesses usually go unnoticed. However, both are a key part of the final pose estimation. In this work, we present DA-LMR (Delta-Angle Lane Marking Representation), a robust data representation in the context of localization approaches. We propose a representation of lane markings that encodes how a curve changes in each point and includes this information in an additional dimension, thus providing a more detailed geometric structure description of the data. We also propose DC-SAC (Distance-Compatible Sample Consensus), a data association method. This is a heuristic version of RANSAC that dramatically reduces the hypothesis space by distance compatibility restrictions. We compare the presented methods with some state-of-the-art data representation and data association approaches in different noisy scenarios. The DA-LMR and DC-SAC produce the most promising combination among those compared, reaching 98.1% in precision and 99.7% in recall for noisy data with 0.5 m of standard deviation.