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

arXiv: https://arxiv.org/abs/2111.09230 · HTML: https://mlsanity.com/paper/2111.09230/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2021.

## Built on by (most cited first, 3 of 3)

- [Robust Self-Tuning Data Association for Geo-Referencing Using Lane Markings](https://mlsanity.com/paper/2207.14042/) (2022)
- [Geo-Localization Based on Dynamically Weighted Factor-Graph](https://mlsanity.com/paper/2311.07301/) (2023)
- [YOLinO++: Single-Shot Estimation of Generic Polylines for Mapless Automated Diving](https://mlsanity.com/paper/2402.00989/) (2024)

## Abstract (arXiv)

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.