CSR: A Lightweight Crowdsourced Road Structure Reconstruction System for Autonomous Driving

Abstract

Highly accurate and robust vectorized reconstruction of road structures is crucial for autonomous vehicles. Traditional LiDAR-based methods require multiple processes and are often expensive, time-consuming, labor-intensive, and cumbersome. In this paper, we propose a lightweight crowdsourced road structure reconstruction system (termed CSR) that relies solely on online perceived semantic elements. Ambiguities and perceptual errors of semantic features and Global Navigation Satellite System (GNSS) global pose errors constitute the predominant challenge in achieving alignment across multi-trip data. To this end, a robust two-phased coarse-to-fine multi-trip alignment method is performed considering local geometric consistency, global topology consistency, intra-trip temporal consistency, and inter-trip consistency. Further, we introduce an incremental pose graph optimization framework with adaptive weight tuning ability to integrate pre-built road structures, currently perceived multi-trip semantic features, odometry, and GNSS, enabling accurate and robust incremental road structure reconstruction. CSR is highly automated, efficient, and scalable for large-scale autonomous driving scenarios, significantly expediting road structure production. We quantitatively and qualitatively validate the reconstruction performance of CSR in real-world scenes. CSR achieves centimeter-level accuracy commensurate with established LiDAR-based methods, concurrently boosting efficiency and reducing resource expenditure.