Article

Keyframe Management for Dense Maps on Memory-Constrained Robots

The authors propose a keyframe management method for dense maps on memory-constrained robots that directly limits the memory occupied by the resident map and admits and releases keyframes by semantic spatial units such as building rooms and corridors. Across every run on two NVIDIA Jetson boards, the memory budget was never exceeded and model throughput loss was at most 0.8%, whereas without the method the operating system terminates the model or the map. Under that budget, admission by semantic zone localized 6-9 percentage points more query frames than geometric partitioning by the number of matched regions, roughly twice that of RTAB-Map retrieval, with lower ATE.

Part of the event: A New Method for Keyframe Management in Dense Mapping for Memory-Constrained Robots

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