Factor Graph / World Model Lab
Learn one visual language for four systems: GraphSLAM, energy-based models, latent world models, and action-conditioned video simulators.
Factor graph / world model lab
When loop closures really anchor the map
Raise the amber loop factor and blue-green measurement coverage while watching whether the latent pose chain snaps into a globally consistent solve.
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Start with a drifting chain, then raise loop closure while coverage stays healthy. The whole map should share the correction instead of leaving drift trapped in the last segment.
Watch for
Observed circles and rectangular factors stay fixed. Only the hollow pose and landmark nodes are the geometry the solver is trying to recover.
Diagram language
How to read it
Observed circles feed local constraint factors. Only the hollow pose and landmark nodes move during the solve.
Current math lens
Diagnosis
Well-anchored sparse solve
The graph has both local measurement support and a strong long-range revisit. That is the sweet spot: the optimizer can distribute correction over the whole map instead of just polishing local edges.
ML translation
High measurement coverage plus a strong loop closure turns the information matrix from nearly chain-structured into globally informative.
Where this matters
This is what mature pose-graph backends aim for when they aggressively validate loop closures before adding them.