MR.ScaleMaster: Scale-Consistent Collaborative Mapping from Crowd-Sourced Monocular Videos

Title:MR.ScaleMaster: Scale-Consistent Collaborative Mapping from Crowd-Sourced Monocular Videos

Abstract:Crowd-sourced cooperative mapping combines monocular sessions from different front-ends, each an independently reconstructed keyframe sequence. Each session has its own local coordinate frame and may follow an incompatible scale convention. We present this http URL, a backend that accepts image, Sim(3) pose, and point-map packets without requiring a common reconstruction model, camera intrinsics, or front-end-provided metric scale. Our Cross-Front-End Loop Factor (CFL) uses a shared matcher for image correspondences but retrieves matched 3D points from the input point maps, so its scale estimates the inter-session ratio. Scale Preconditioning (SPC) initializes session scales for a Sim(3) anchor-node graph, which then corrects residual scale and drift. For front-ends exposing an incremental scale trajectory, an agent-side Scale Collapse Alarm (SCA) rejects or rolls back false intra-session loops that would otherwise collapse the session scale. We evaluate seven front-ends on KITTI and five on CODa. Based on ground-truth path lengths, session scales in our heterogeneous KITTI setting differ by 57-103x. CFL reduces mean ATE by 37% and inter-session scale error by 60% relative to loop factors from independent pairwise reconstructions. Adding SPC raises the reductions to 74% and 77%, respectively. On CODa, five-session fusion improves mean ATE over single-session runs for all five front-ends, while joint fusion registers 15 sessions from three different front-ends in a single map. Code will be released.

Robotics (cs.RO)

arXiv:2604.11372 [cs.RO]

(or arXiv:2604.11372v4 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2604.11372 Focus to learn more arXiv-issued DOI via DataCite

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