SG-AMP: Scene-Graph-Guided Active Perception and Semantics-Aware Motion Planning for Pepper Plants

2026-09-01

AI Summary

该研究通过结合深度补全、全景映射、场景图推理及语义感知运动规划，解决了辣椒植株的主动感知与规划问题。

Abstract

We present SG-AMP, integrating robust depth completion with input-conditioned uncertainty, persistent panoptic mapping, plant scene-graph reasoning, and semantics-aware active view-motion planning. Beyond inspecting uncertain observed regions, the scene graph explicitly hypothesizes unobserved pepper--peduncle attachments and directs close-range sensing toward them. Candidate views are selected according to expected information gain, while class-dependent motion costs distinguish protected peppers, peduncles, and stems from conditionally traversable foliage. On pepper data, the perception network achieves $55.27\%$ semantic mIoU, $38.67\%$ PQ, and $40.62\,\mathrm{mm}$ depth RMSE, while input-conditioned uncertainty improves NYUv2 NLL from $-1.6518$ to $-1.6925$ and AUSE from $0.0102$ to $0.0087$.

Problem

• Scene-Graph
• Active Perception
• Semantics-Aware Motion Planning
• Pepper Plants

Innovation

• Scene-Graph-Guided
• Semantics-Aware Motion Planning
• Depth Completion with Uncertainty
• Persistent Panoptic Mapping
• Active Perception