GAP-GDRNet: Geometry-Aware Monocular Visual Pose Sensing on a Single-Target Synthetic Spacecraft Dataset
arXiv:2607.02360v1 Announce Type: cross Abstract: Monocular relative pose sensing is a central perception problem in non-cooperative rendezvous and on-orbit servicing. In spacecraft images, however, weak surface texture, thin appendages, illumination changes, and partial occlusion often leave only sparse and unstable geometric evidence. This article presents GAP-GDRNet, a geometry-aware attention-enhanced framework for monocular RGB-based 6D pose sensing. The method follows the geometry-guided direct regression paradigm of GDR-Net and modifies two points in the pipeline: an attention-based fea
The continuous development in computer vision and deep learning allows for more sophisticated and robust monocular pose sensing, critical for autonomous space operations.
Improved monocular pose sensing is vital for non-cooperative rendezvous and on-orbit servicing, enabling more autonomous and complex space missions with reduced external dependency.
The ability to accurately determine 6D pose from single RGB images, even with challenging visual conditions in space, becomes more reliable, potentially lowering mission costs and complexity.
- · Space agencies
- · Satellite operators
- · Defense contractors
- · AI/computer vision developers
- · Traditional rendezvous sensor manufacturers
- · Systems heavily reliant on multi-sensor redundancy
Enhanced autonomy for satellite servicing and debris removal becomes feasible with precise monocular pose sensing.
Reduced operational costs and increased mission flexibility for in-orbit logistics and assembly due to improved visual guidance.
Accelerated development of fully autonomous, self-reproducing space infrastructure and extended lunar/Mars missions reliant on minimal human intervention.
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Read at arXiv cs.AI