Online Mapping for Autonomous Driving: Addressing Sensor Generalization and Dynamic Map Updates in Campus Environments

2025-09-29

AI Summary

To address the high construction cost, infrequent updates, and poor sensor generalization of conventional high-definition (HD) maps in dynamic campus environments, this paper proposes a real-time online mapping method leveraging tightly coupled stereo camera and LiDAR fusion. The core contribution is a lightweight semantic vector map (SemVecMap) framework, integrating multi-sensor spatiotemporal calibration, 3D semantic segmentation, and incremental map updating—fine-tuned on campus-specific data to enhance generalization. Evaluated on a real-world golf-cart autonomous platform, the system achieves sub-meter-accurate, real-time 3D HD map generation with adaptive updates under dynamic conditions. It significantly reduces manual intervention, improves map freshness and scene adaptability, and delivers a practical, lightweight mapping solution tailored for autonomous driving in confined, semi-structured environments such as university campuses.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Multi-modal Vision

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

High-definition (HD) maps are essential for autonomous driving, providing precise information such as road boundaries, lane dividers, and crosswalks to enable safe and accurate navigation. However, traditional HD map generation is labor-intensive, expensive, and difficult to maintain in dynamic environments. To overcome these challenges, we present a real-world deployment of an online mapping system on a campus golf cart platform equipped with dual front cameras and a LiDAR sensor. Our work tackles three core challenges: (1) labeling a 3D HD map for campus environment; (2) integrating and generalizing the SemVecMap model onboard; and (3) incrementally generating and updating the predicted HD map to capture environmental changes. By fine-tuning with campus-specific data, our pipeline produces accurate map predictions and supports continual updates, demonstrating its practical value in real-world autonomous driving scenarios.

Problem

• Online mapping for autonomous driving in campus environments
• Addressing sensor generalization and dynamic map updates
• Generating and updating HD maps incrementally for changes

Innovation

• Online mapping system with dual cameras and LiDAR
• Fine-tuning SemVecMap model for campus environments
• Incremental HD map generation for dynamic updates