Research
SG-AMP: Scene-Graph-Guided Active Perception and Semantics-Aware Motion Planning for Pepper Plants
arXiv:2609.01579v1 Announce Type: new Abstract: We present SG-AMP, integrating robust depth completion with input-conditioned uncertainty, persistent panoptic mapping, plant scene-graph reasoning, and
arXiv:2609.01579v1 Announce Type: new 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,mm depth RMSE, while input-conditioned uncertainty improves NYUv2 NLL from -1.6518 to -1.6925 and AUSE from 0.0102 to 0.0087.
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Source: arXiv cs.RO | 2026-09-02