# RoadMap: A Light-Weight Semantic Map for Visual Localization towards Autonomous Driving arXiv: https://arxiv.org/abs/2106.02527 ยท HTML: https://mlsanity.com/paper/2106.02527/ ## Key facts - Cited by 34 papers in the map (data through 14 Oct 2026). - Builds on 10 of its 26 references that are in the map. - First posted on arXiv 4 Jun 2021. ## Builds on (most cited first, 10 of 10) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015) - [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014) - [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015) - [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016) - [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017) - [On-Manifold Preintegration for Real-Time Visual-Inertial Odometry](https://mlsanity.com/paper/1512.02363/) (2015) - [maplab: An Open Framework for Research in Visual-inertial Mapping and Localization](https://mlsanity.com/paper/1711.10250/) (2017) - [AVP-SLAM: Semantic Visual Mapping and Localization for Autonomous Vehicles in the Parking Lot](https://mlsanity.com/paper/2007.01813/) (2020) - [Relocalization, Global Optimization and Map Merging for Monocular Visual-Inertial SLAM](https://mlsanity.com/paper/1803.01549/) (2018) - [Appearance-Based Landmark Selection for Efficient Long-Term Visual Localization](https://mlsanity.com/paper/1808.02656/) (2018) ## Built on by (most cited first, 30 of 34) - [RoMe: Towards Large Scale Road Surface Reconstruction via Mesh Representation](https://mlsanity.com/paper/2306.11368/) (2023) - [MMFN: Multi-Modal-Fusion-Net for End-to-End Driving](https://mlsanity.com/paper/2207.00186/) (2022) - [A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services](https://mlsanity.com/paper/2304.14271/) (2023) - [Deep Learning for Visual Localization and Mapping: A Survey](https://mlsanity.com/paper/2308.14039/) (2023) - [Prompting Multi-Modal Tokens to Enhance End-to-End Autonomous Driving Imitation Learning with LLMs](https://mlsanity.com/paper/2404.04869/) (2024) - [BEV-Locator: An End-to-end Visual Semantic Localization Network Using Multi-View Images](https://mlsanity.com/paper/2211.14927/) (2022) - [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) - [A Vision-Centric Approach for Static Map Element Annotation](https://mlsanity.com/paper/2309.11754/) (2023) - [Crowd-Sourced NeRF: Collecting Data from Production Vehicles for 3D Street View Reconstruction](https://mlsanity.com/paper/2406.16289/) (2024) - [SketchSplat: 3D Edge Reconstruction via Differentiable Multi-view Sketch Splatting](https://mlsanity.com/paper/2503.14786/) (2025) - [How to Build a Curb Dataset with LiDAR Data for Autonomous Driving](https://mlsanity.com/paper/2110.03968/) (2021) - [Construction and application of artificial intelligence crowdsourcing map based on multi-track GPS data](https://mlsanity.com/paper/2402.15796/) (2024) - [ImPosing: Implicit Pose Encoding for Efficient Visual Localization](https://mlsanity.com/paper/2205.02638/) (2022) - [FlowMap: Path Generation for Automated Vehicles in Open Space Using Traffic Flow](https://mlsanity.com/paper/2305.01622/) (2023) - [MaskFuser: Masked Fusion of Joint Multi-Modal Tokenization for End-to-End Autonomous Driving](https://mlsanity.com/paper/2405.07573/) (2024) - [Towards Accurate Ground Plane Normal Estimation from Ego-Motion](https://mlsanity.com/paper/2212.04224/) (2022) - [Why semantics matters: A deep study on semantic particle-filtering localization in a LiDAR semantic pole-map](https://mlsanity.com/paper/2305.14038/) (2023) - [Online Temporal Fusion for Vectorized Map Construction in Mapless Autonomous Driving](https://mlsanity.com/paper/2409.00593/) (2024) - [You Only Label Once: 3D Box Adaptation from Point Cloud to Image via Semi-Supervised Learning](https://mlsanity.com/paper/2211.09302/) (2022) - [DeepSTEP -- Deep Learning-Based Spatio-Temporal End-To-End Perception for Autonomous Vehicles](https://mlsanity.com/paper/2305.06820/) (2023) - [NeRO: Neural Road Surface Reconstruction](https://mlsanity.com/paper/2405.10554/) (2024) - [Monocular Localization with Semantics Map for Autonomous Vehicles](https://mlsanity.com/paper/2406.03835/) (2024) - [Neural Semantic Map-Learning for Autonomous Vehicles](https://mlsanity.com/paper/2410.07780/) (2024) - [Maps from Motion (MfM): Generating 2D Semantic Maps from Sparse Multi-view Images](https://mlsanity.com/paper/2411.12620/) (2024) - [Maps for Autonomous Driving: Full-process Survey and Frontiers](https://mlsanity.com/paper/2509.12632/) (2025) - [Sparse Semantic Map-Based Monocular Localization in Traffic Scenes Using Learned 2D-3D Point-Line Correspondences](https://mlsanity.com/paper/2210.04543/) (2022) - [A Real-Time Fusion Framework for Long-term Visual Localization](https://mlsanity.com/paper/2210.09757/) (2022) - [CMSG Cross-Media Semantic-Graph Feature Matching Algorithm for Autonomous Vehicle Relocalization](https://mlsanity.com/paper/2305.08318/) (2023) - [NocPlace: Nocturnal Visual Place Recognition via Generative and Inherited Knowledge Transfer](https://mlsanity.com/paper/2402.17159/) (2024) ## Abstract (arXiv) Accurate localization is of crucial importance for autonomous driving tasks. Nowadays, we have seen a lot of sensor-rich vehicles (e.g. Robo-taxi) driving on the street autonomously, which rely on high-accurate sensors (e.g. Lidar and RTK GPS) and high-resolution map. However, low-cost production cars cannot afford such high expenses on sensors and maps. How to reduce costs? How do sensor-rich vehicles benefit low-cost cars? In this paper, we proposed a light-weight localization solution, which relies on low-cost cameras and compact visual semantic maps. The map is easily produced and updated by sensor-rich vehicles in a crowd-sourced way. Specifically, the map consists of several semantic elements, such as lane line, crosswalk, ground sign, and stop line on the road surface. We introduce the whole framework of on-vehicle mapping, on-cloud maintenance, and user-end localization. The map data is collected and preprocessed on vehicles. Then, the crowd-sourced data is uploaded to a cloud server. The mass data from multiple vehicles are merged on the cloud so that the semantic map is updated in time. Finally, the semantic map is compressed and distributed to production cars, which use this map for localization. We validate the performance of the proposed map in real-world experiments and compare it against other algorithms. The average size of the semantic map is 36 kb/km. We highlight that this framework is a reliable and practical localization solution for autonomous driving.