Lifelong LERF: Local 3D Semantic Inventory Monitoring Using FogROS2
CoRR(2024)
摘要
Inventory monitoring in homes, factories, and retail stores relies on
maintaining data despite objects being swapped, added, removed, or moved. We
introduce Lifelong LERF, a method that allows a mobile robot with minimal
compute to jointly optimize a dense language and geometric representation of
its surroundings. Lifelong LERF maintains this representation over time by
detecting semantic changes and selectively updating these regions of the
environment, avoiding the need to exhaustively remap. Human users can query
inventory by providing natural language queries and receiving a 3D heatmap of
potential object locations. To manage the computational load, we use Fog-ROS2,
a cloud robotics platform, to offload resource-intensive tasks. Lifelong LERF
obtains poses from a monocular RGBD SLAM backend, and uses these poses to
progressively optimize a Language Embedded Radiance Field (LERF) for semantic
monitoring. Experiments with 3-5 objects arranged on a tabletop and a Turtlebot
with a RealSense camera suggest that Lifelong LERF can persistently adapt to
changes in objects with up to 91
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