Maps assume the world holds still. It doesn’t: chairs move, doors close, cars leave, and a field looks different after the harvest. A robot that runs for weeks needs a map that knows what changed, what probably changed, and what it hasn’t looked at for a while.
Representations
The scene graph survey sorts the options:
- A full graph per time step. Easy to learn from, wasteful, since everything static is copied every step.
- A node per observation (Khronos). The history is kept, but something has to decide which observations are the same object.
- Nodes with attributes that change over time (Kimera, CURB-SG). Compact, and the most common, but one wrong data association corrupts the history of that node.
Absence
Khronos separates absence of evidence (I didn’t see the chair) from evidence of absence (I looked where the chair was and saw the wall behind it). It casts camera rays, and calls an object gone when 60% of the rays in a 5 s window pass through where it used to be. Change detection F1 is 62.0, against 38.6 for Panoptic Mapping. SuperMap keeps a log-odds existence value per map point instead, and reports change recall of 0.62 to 1.00 on a ten-minute real run. 3D VSG tries to predict which nodes will change (77.1% accuracy), but change is rare (13% to 21% of nodes) and it runs on ground-truth graphs.
Khronos also found that swapping in open-set segmentation barely moved its change-detection F1 (64.4 against 64.6). The hard part is the bookkeeping.
Limits
- The ray test needs a surface behind the object. In an open field or open air there is no back wall, so there is no evidence of absence, only of not seeing.
- Memory grows without bound. Nobody has shown a scene graph running for months with a principled way to forget.
- Consistency is checked geometrically. The survey points out that nobody checks whether a node’s embedding, attributes and relations still agree from one visit to the next, which could catch changes that geometry misses.
- With several robots, “it moved” and “the other robot is wrong” look the same. Telling them apart needs a model of how much to trust each robot (resilient consensus, Byzantine faults).
Questions
- When should a frequently moved object stop being a node and become a distribution over its room?
- How long can a robot trust an observation it made last week?
- Can a team divide the work of re-checking a shared map, so each part is seen often enough?
Related: long-term autonomy, 3D scene graphs, collaborative SLAM.