DLR Portal
Home|Sitemap|Contact|Accessibility Imprint and terms of use Privacy Cookies & Tracking |Deutsch
You are here: Home:IMF:Department: Photogrammetry and Image Analysis
Advanced Search
Earth Observation Center
DFD
IMF
Department: Atmospheric Processors
Department: EO Data Science
Department: Experimental Methods
Department: Photogrammetry and Image Analysis
Team: Projekte und Missionen
Team: 3D Modeling
Team: Traffic Monitoring
Team: Hyperspectral Remote Sensing
Team: Optical Remote Sensing of Water (BA)
Team: Young Researchers Group ARIADNE
Department: SAR Signal Processing
Services
Technology
Applications and Projects
Satellite Data
Media Library
News / Archive
Blogs & Features
Jobs
Print

HD Maps: Fine-grained Road Segmentation by Parsing Ground and Aerial Images



Creating detailed road maps

Currently used road maps are intended for the navigation of humans. The roads are stored as centerlines with connections to other roads without providing detailed information about the physical dimensions of the road, the number and width of lanes and the presence of sidewalks or parking spots. Creating maps containing also this information is important for infrastructure planning, creating realistic simulations over roads and supporting autonomous driving by improving the scene understanding and allowing precise self-localization.

Method

The paper in downloads presents an approach for augmenting the road maps with fine-grained categories by applying existing road maps, aerial images and ground images jointly. The proposed approach considers both the image evidence and the constraints on the road layout and the size of lanes. This allows robust estimation also in case when lane markings are not visible. The alignment of ground and aerial images is necessary as even when applying sophisticated GPS-IMU systems, registration errors can still occur. The registration of the ground images to the map can also function as precise self-localization of the vehicle within the road, an important task for path planning and safe driving.

Example for the output of the method. Left: aerial images. Right: ground images. Pink: road, yellow: parking spots, blue: sidewalk, red: the road detection in ground projected into the aerial image

Air-Ground-KITTI dataset

The dataset of annotated aerial and ground images used in the experiments is provided at downloads.

Figure 2: Examples of the dataset. Left: aerial and ground images. Right: the corresponding annotations (road, parking sports, sidewalk, building).

 

 

 

 

 

 

 

 

 
 

If you use the dataset, please cite our paper listed at links on the right.


Contact
Dr.-Ing. Franz Kurz
German Aerospace Center (DLR)

Remote Sensing Technology Institute
, Photogrammetry and Image Analysis
Weßling

Tel.: +49 8153 28-2764

Links
Paper:
HD Maps: Fine-grained Road Segmentation by Parsing Ground and Aerial Images
Download:
HD Maps Public Dataset (146 MB)
Copyright © 2023 German Aerospace Center (DLR). All rights reserved.