Structure from Motion-Based Mapping for Autonomous Driving: Practice and Experience

Accurate and up-to-date 3D maps, often represented as point clouds, are crucial for autonomous vehicles. Crowd-sourcing has emerged as a low-cost and scalable approach for collecting mapping data utilizing widely available dashcams and other sensing devices. However, it is still a non-trivial task to utilize crowdsourced data, such as dashcam images and video, to efficiently create or update high-quality point clouds using technologies like Structure from Motion (SfM). This study assesses and compares different image matching options available in open-source SfM software, analyzing their applicability and limitations for mapping urban scenes in different practical scenarios. Furthermore, the study analyzes the impact of various camera setups (i.e., the number of cameras and their placement) and weather conditions on the quality of the generated 3D point clouds in terms of completeness and accuracy. Based on these analyses, our study provides guidelines for creating more accurate point clouds.

DOI

10.1145/3631533

Citation

Zhanabatyrova, A, Souza Leite, C & Xiao, Y 2024, 'Structure from Motion-Based Mapping for Autonomous Driving: Practice and Experience', ACM Transactions on the Internet of Things, vol. 5, no. 1, 6. https://doi.org/10.1145/3631533

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-202402072336

Collections

[article-cris] Palvelut / Services

Show all metadata

Structure from Motion-Based Mapping for Autonomous Driving: Practice and Experience

Access rights

URL

Journal Title

Journal ISSN

Volume Title

Authors

Date

Department

Major/Subject

Mcode

Degree programme

Language

Pages

Series

Abstract

Description

Keywords

Other note

DOI

Citation

Permanent link to this item

Collections