# Road Mapping and Localization using Sparse Semantic Visual Features

arXiv: https://arxiv.org/abs/2108.05047 · HTML: https://mlsanity.com/paper/2108.05047/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2021.

## Built on by (most cited first, 7 of 7)

- [3D Neural Edge Reconstruction](https://mlsanity.com/paper/2405.19295/) (2024)
- [EgoVM: Achieving Precise Ego-Localization using Lightweight Vectorized Maps](https://mlsanity.com/paper/2307.08991/) (2023)
- [SketchSplat: 3D Edge Reconstruction via Differentiable Multi-view Sketch Splatting](https://mlsanity.com/paper/2503.14786/) (2025)
- [RTMap: Real-Time Recursive Mapping with Change Detection and Localization](https://mlsanity.com/paper/2507.00980/) (2025)
- [Maps from Motion (MfM): Generating 2D Semantic Maps from Sparse Multi-view Images](https://mlsanity.com/paper/2411.12620/) (2024)
- [CGGT: Curve-Grounded Geometry Transformer for 3D Parametric Curve Reconstruction](https://mlsanity.com/paper/2609.14521/) (2026)
- [From Fragments to Global Maps: Learning Vectorized Map Aggregation with Large Language Models](https://mlsanity.com/paper/2610.02513/) (2026)

## Abstract (arXiv)

We present a novel method for visual mapping and localization for autonomous vehicles, by extracting, modeling, and optimizing semantic road elements. Specifically, our method integrates cascaded deep models to detect standardized road elements instead of traditional point features, to seek for improved pose accuracy and map representation compactness. To utilize the structural features, we model road lights and signs by their representative deep keypoints for skeleton and boundary, and parameterize lanes via piecewise cubic splines. Based on the road semantic features, we build a complete pipeline for mapping and localization, which includes a) image processing front-end, b) sensor fusion strategies, and c) optimization backend. Experiments on public datasets and our testing platform have demonstrated the effectiveness and advantages of our method by outperforming traditional approaches.