AI
CARD: A Proof-Carrying Certificate-to-Action Interface for Relative-Measurement Graphs
Publication: cs.AI updates on arXiv.org
Author: Yushan Li
Source published: 04/11/2026, 05:00:00 UTC
Briefing created: 04/11/2026, 08:08:46 UTC
Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 04/11/2026, 05:18:23 UTC.
Briefings
CARD is a proof-carrying interface for relative-measurement graphs that separates semantic motion from witness repair. It uses a lifted-fiber formulation to characterize when projected and proof-carrying costs coincide. The primary study analyzed 404 checker-confirmed optimizer points from an AA15-X500 airborne survey system, finding median rotation discrepancies of 1.9891 degrees and translation discrepancies of 21.6322 meters. Public downstream evaluation on 11 KITTI, EuRoC, and TUM SLAM cases shows optimizer wall-time ratios ranging from 1.128x to 67.65x for random versus CARD graph interventions.
Key points
- CARD uses a lifted-fiber formulation to separate semantic motion from witness repair in relative-measurement graphs.
- The AA15-X500 airborne survey system study analyzed 404 checker-confirmed optimizer points with median rotation discrepancies of 1.9891 degrees and translation discrepancies of 21.6322 meters.
Context
This research addresses the gap between geometric certificates and robotic action, presenting CARD as a proof-carrying interface that records how objects combine. The findings are critical for understanding the exact service laws governing sampling strategies in relative-measurement graphs.
Limitations
The primary study is limited to 404 cases from a single airborne survey system. Public downstream evaluation data is restricted to 11 specific SLAM cases, and the evidence does not include additional background on the broader implications of the findings.
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