SafeMap: Robust HD Map Construction from Incomplete Observations

TL;DR

SafeMap is a new framework for robust HD map construction in autonomous driving that effectively handles incomplete camera data by integrating view importance prioritization and BEV correction modules.

Contribution

SafeMap introduces a novel end-to-end approach combining G-PVR and D-BEVC modules to improve HD map accuracy with incomplete observations.

Findings

Outperforms previous methods in incomplete scenarios

Enhances robustness of HD map construction

Easily integrates into existing systems

Abstract

Robust high-definition (HD) map construction is vital for autonomous driving, yet existing methods often struggle with incomplete multi-view camera data. This paper presents SafeMap, a novel framework specifically designed to secure accuracy even when certain camera views are missing. SafeMap integrates two key components: the Gaussian-based Perspective View Reconstruction (G-PVR) module and the Distillation-based Bird's-Eye-View (BEV) Correction (D-BEVC) module. G-PVR leverages prior knowledge of view importance to dynamically prioritize the most informative regions based on the relationships among available camera views. Furthermore, D-BEVC utilizes panoramic BEV features to correct the BEV representations derived from incomplete observations. Together, these components facilitate the end-to-end map reconstruction and robust HD map generation. SafeMap is easy to implement and integrates seamlessly into existing systems, offering a plug-and-play solution for enhanced robustness. Experimental results demonstrate that SafeMap significantly outperforms previous methods in both complete and incomplete scenarios, highlighting its superior performance and reliability.