HGeo-TopoMap: Boosting Topological Mapping with Hierarchical Geometric Priors

2026-07-23 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

HGeo-TopoMap is a novel method designed to enhance topological mapping in autonomous driving perception systems, specifically addressing the challenge of detecting centerline instances where explicit road markings are absent. It employs a hierarchical approach, leveraging an explicit prior map and implicit spatial relations. The system integrates a geometric adaptive learning module, which discretely encodes semantic and spatial features from inverse perspective mapped road structures using a prior-mask attention mechanism. Additionally, a geometric consistency learning module utilizes centerline geometric properties and spatial relationships, enforcing consistency by aligning features with identical geometric orientations via a geometry-aware decoder. Evaluated on the OpenLane-V2 dataset across centerline, lane segment, and robustness benchmarks, HGeo-TopoMap demonstrates substantial improvements in topological mapping accuracy and enhanced robustness, consistently outperforming baseline methods under various conditions.

Key takeaway

For autonomous driving perception system engineers focused on robust topological mapping, HGeo-TopoMap offers a significant advancement in centerline detection. You should consider integrating its hierarchical geometric priors and consistency learning modules to improve accuracy and enhance system robustness, especially in environments lacking explicit road markings. This approach can lead to more reliable path planning and navigation.

Key insights

HGeo-TopoMap boosts topological mapping accuracy and robustness using hierarchical geometric priors and spatial relations.

Principles

• Leverage explicit prior maps and implicit spatial relations.
• Discretely encode semantic and spatial features.
• Enforce spatial consistency via geometric orientations.

Method

HGeo-TopoMap uses a geometric adaptive learning module with prior-mask attention and a geometric consistency learning module built on a geometry-aware decoder.

In practice

• Apply prior-mask attention for feature selection.
• Align centerline features by geometric orientation.
• Evaluate on OpenLane-V2 dataset benchmarks.

Topics

• Autonomous Driving
• Topological Mapping
• Centerline Detection
• Geometric Priors
• Computer Vision
• OpenLane-V2

Code references

• lynn-yu/HGeo-TopoMap

Best for: Research Scientist, AI Scientist, Computer Vision Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.