🤖 AI Summary
This work addresses the lack of environment representation methods for mobile manipulators under edge computing constraints that simultaneously ensure timeliness, semantic richness, and geometric reliability. The authors propose KRVF, a task-oriented voxelized world representation that integrates occupancy, color, semantic evidence, temporal recency, and data provenance to enable closed-loop feedback between mapping and perception. Key innovations include decoupling observed occupancy from semantic priors to enhance object reasoning robustness under depth failure, introducing a map-prior-driven depth inpainting mechanism, and designing a task-aware semantic query interface. Built upon semantic voxel representations, source-aware fusion, and real-time ROS 2 integration, the system efficiently supports semantic object retrieval and grasp candidate generation on edge devices.
📝 Abstract
Mobile manipulators need world models that are current, queryable, semantically meaningful, and usable under edge-compute constraints. This technical report presents KRVF, a source-aware semantic voxel world representation for edge mobile manipulation. Unlike reconstruction-centric mapping pipelines that primarily optimize global geometric fidelity, KRVF represents local world state as task-oriented voxels that encode occupancy, color, semantic evidence, temporal freshness, and evidence source. The representation separates measured occupancy from semantic-prior hypotheses, enabling depth-failure-aware object reasoning without silently corrupting persistent geometry. KRVF also closes a feedback loop between mapping and sensing by rendering map-prior depth for repair, and exposes task-level query operators for semantic objects and grasp candidates. The report formalizes the KRVF representation and documents a ROS 2 implementation that turns online RGB-D observations into a task-facing robot memory.