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Lifelong LERF: Local 3D Semantic Inventory Monitoring Using FogROS2
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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% accuracy.
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Cited by 1 Pith paper
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Locate 3D: Real-World Object Localization via Self-Supervised Learning in 3D
LOCATE 3D localizes objects named in referring expressions directly from posed RGB-D frames, achieving 61.7% top-1 accuracy at IoU 25 on SR3D/NR3D/ScanRefer and 63.7% when trained with a new 130K-annotation dataset.
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