# Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization

arXiv: https://arxiv.org/abs/1908.03274 · HTML: https://mlsanity.com/paper/1908.03274/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2019.

## Built on by (most cited first, 24 of 24)

- [Learning from All Vehicles](https://mlsanity.com/paper/2203.11934/) (2022)
- [Structured Bird's-Eye-View Traffic Scene Understanding from Onboard Images](https://mlsanity.com/paper/2110.01997/) (2021)
- [OrienterNet: Visual Localization in 2D Public Maps with Neural Matching](https://mlsanity.com/paper/2304.02009/) (2023)
- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [Topology Preserving Local Road Network Estimation from Single Onboard Camera Image](https://mlsanity.com/paper/2112.10155/) (2021)
- [MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square](https://mlsanity.com/paper/2102.03771/) (2021)
- [Deep Multi-Task Learning for Joint Localization, Perception, and Prediction](https://mlsanity.com/paper/2101.06720/) (2021)
- [Walking Your LiDOG: A Journey Through Multiple Domains for LiDAR Semantic Segmentation](https://mlsanity.com/paper/2304.11705/) (2023)
- [Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments](https://mlsanity.com/paper/2208.07364/) (2022)
- [BEV-Locator: An End-to-end Visual Semantic Localization Network Using Multi-View Images](https://mlsanity.com/paper/2211.14927/) (2022)
- [EgoVM: Achieving Precise Ego-Localization using Lightweight Vectorized Maps](https://mlsanity.com/paper/2307.08991/) (2023)
- [Improving Online Lane Graph Extraction by Object-Lane Clustering](https://mlsanity.com/paper/2307.10947/) (2023)
- [Pix2Map: Cross-modal Retrieval for Inferring Street Maps from Images](https://mlsanity.com/paper/2301.04224/) (2023)
- [Coarse-to-fine Semantic Localization with HD Map for Autonomous Driving in Structural Scenes](https://mlsanity.com/paper/2107.02557/) (2021)
- [Structure from Duplicates: Neural Inverse Graphics from a Pile of Objects](https://mlsanity.com/paper/2401.05236/) (2024)
- [Energy-Based Models for Cross-Modal Localization using Convolutional Transformers](https://mlsanity.com/paper/2306.04021/) (2023)
- [Translating Images to Road Network: A Sequence-to-Sequence Perspective](https://mlsanity.com/paper/2402.08207/) (2024)
- [Why semantics matters: A deep study on semantic particle-filtering localization in a LiDAR semantic pole-map](https://mlsanity.com/paper/2305.14038/) (2023)
- [Prior Based Online Lane Graph Extraction from Single Onboard Camera Image](https://mlsanity.com/paper/2307.13344/) (2023)
- [Semantic Image Alignment for Vehicle Localization](https://mlsanity.com/paper/2110.04162/) (2021)
- [Online Lane Graph Extraction from Onboard Video](https://mlsanity.com/paper/2304.00930/) (2023)
- [Semantic and Articulated Pedestrian Sensing Onboard a Moving Vehicle](https://mlsanity.com/paper/2309.06313/) (2023)
- [Robust Vehicle Localization and Tracking in Rain using Street Maps](https://mlsanity.com/paper/2409.01038/) (2024)
- [Robust Robotic Exploration and Mapping Using Generative Occupancy Map Synthesis](https://mlsanity.com/paper/2506.20049/) (2025)

## Abstract (arXiv)

In this paper we propose a novel semantic localization algorithm that exploits multiple sensors and has precision on the order of a few centimeters. Our approach does not require detailed knowledge about the appearance of the world, and our maps require orders of magnitude less storage than maps utilized by traditional geometry- and LiDAR intensity-based localizers. This is important as self-driving cars need to operate in large environments. Towards this goal, we formulate the problem in a Bayesian filtering framework, and exploit lanes, traffic signs, as well as vehicle dynamics to localize robustly with respect to a sparse semantic map. We validate the effectiveness of our method on a new highway dataset consisting of 312km of roads. Our experiments show that the proposed approach is able to achieve 0.05m lateral accuracy and 1.12m longitudinal accuracy on average while taking up only 0.3% of the storage required by previous LiDAR intensity-based approaches.