生涯LERF：FogROS2を用いた局所的3Dセマンティック在庫モニタリング
Lifelong LERF: Local 3D Semantic Inventory Monitoring Using FogROS2
移動ロボットが言語と幾何の密な表現を継続的に最適化し、物体の変化を検出して選択的に更新することで、自然言語クエリに応じて3Dヒートマップで在庫位置を提示する手法を提案。
著者: Adam Rashid, Chung Min Kim, Justin Kerr, Letian Fu, Kush Hari, Ayah Ahmad, Kaiyuan Chen, Huang Huang, Marcus Gualtieri, Michael Wang, Christian Juette, Nan Tian, Liu Ren, Ken Goldberg
分類: cs.RO
原文アブストラクト
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.
