HD-maps as Prior Information for Globally Consistent Mapping in GPS-denied Environments (2407.19463v1)

Abstract: In recent years, prior maps have become a mainstream tool in autonomous navigation. However, commonly available prior maps are still tailored to control-and-decision tasks, and the use of these maps for localization remains largely unexplored. To bridge this gap, we propose a lidar-based localization and mapping (LOAM) system that can exploit the common HD-maps in autonomous driving scenarios. Specifically, we propose a technique to extract information from the drivable area and ground surface height components of the HD-maps to construct 4DOF pose priors. These pose priors are then further integrated into the pose-graph optimization problem to create a globally consistent 3D map. Experiments show that our scheme can significantly improve the global consistency of the map compared to state-of-the-art lidar-only approaches, proven to be a useful technology to enhance the system's robustness, especially in GPS-denied environment. Moreover, our work also serves as a first step towards long-term navigation of robots in familiar environment, by updating a map. In autonomous driving this could enable updating the HD-maps without sourcing a new from a third party company, which is expensive and introduces delays from change in the world to updated map.

• The paper proposes a method that integrates HD-maps with SLAM to generate pose priors for improved trajectory estimates in challenging GPS-denied settings.
• The paper introduces novel constraints from drivable area and ground height data into pose-graph optimization, yielding globally consistent 3D maps.
• The paper validates its approach using the Argoverse 2 dataset, demonstrating significant accuracy improvements over state-of-the-art LOAM frameworks.

Overview of Applying HD-Maps for SLAM in GPS-Denied Environments

The paper "HD-maps as Prior Information for Globally Consistent Mapping in GPS-denied Environments" by Waqas Ali, Patric Jensfelt, and Thien-Minh Nguyen proposes a novel methodology for enhancing the accuracy and robustness of SLAM systems in environments where GPS signals are compromised. The authors leverage available high-definition (HD) maps, traditionally used for navigation in autonomous vehicles, as a source of prior information to improve lidar-based localization and mapping capabilities. This research is particularly significant in advancing long-term navigation strategies for autonomous systems operating in environments with dynamic changes and GPS limitations.

Methodological Contributions

1. Integration of HD-Maps with SLAM: The authors present a method that integrates components of HD-maps, specifically the drivable area vector map and ground surface height data, into a SLAM framework to refine trajectory estimates. The process utilizes these maps to generate pose priors that subsequently optimize the SLAM pose-graph, resulting in a globally consistent 3D map aligned with global coordinate systems.
2. Pose-Graph Optimization Scheme: A key innovation is the introduction of novel constraints derived from HD-map components into pose-graph optimization. This approach aims to refine the SLAM trajectory, especially in GPS-denied environments, where traditional corrections via loop closures or GPS data are challenged.
3. Practical Application and Evaluation: Empirical validation is conducted using the Argoverse 2 Trust-but-Verify dataset, highlighting significant improvements in mapping accuracy when compared to existing state-of-the-art LOAM frameworks. The results underscore the potential of employing HD-map information to yield precise localization and mapping outcomes.

Implications and Future Directions

The paper addresses an existing gap in autonomous navigation research, specifically the application of HD-maps in improving SLAM processes rather than mere navigation and control tasks. By proving the efficacy of HD-maps in generating pose priors that enhance SLAM mapping consistency and accuracy, the authors pave the way for several practical and theoretical advancements:

• Autonomous Driving: This approach can significantly impact autonomous vehicle operations by enabling more reliable and scalable map updates without depending on costly third-party data acquisitions.
• Robotic Navigation: For broader robotic applications, especially in complex urban settings, the method promises enhancements in both localization accuracy and operational robustness.
• Scalability of SLAM Systems: The research suggests the potential for further scalability of SLAM systems through multi-modal input integration, laying the groundwork for more efficient and resilient mapping architectures.

Conclusions

This paper makes substantial advancements toward improving SLAM systems' performance in scenarios where traditional signal-based navigation aids are unavailable. The integration of HD-map-derived constraints demonstrates a promising direction for future research in autonomous navigation, providing a basis for exploring more diverse and environment-resilient mapping solutions in robotics and intelligent systems. The work also suggests areas for further exploration, such as the handling of dynamic map updates and more extensive evaluations across diverse environmental conditions.