航空セマンティックマッピングにおける能動学習のための情報経路計画
Informative Path Planning for Active Learning in Aerial Semantic Mapping
ベイズ推定でセグメンテーションモデルの不確実性を見積もり、その不確実性を能動学習の目的関数に結びつけてUAVを誘導し、ラベル付けに最も有益な地形画像を効率的に収集する手法を提案した。
著者: Julius Rückin, Liren Jin, Federico Magistri, Cyrill Stachniss, Marija Popović
分類: cs.RO
原文アブストラクト
Semantic segmentation of aerial imagery is an important tool for mapping and earth observation. However, supervised deep learning models for segmentation rely on large amounts of high-quality labelled data, which is labour-intensive and time-consuming to generate. To address this, we propose a new approach for using unmanned aerial vehicles (UAVs) to autonomously collect useful data for model training. We exploit a Bayesian approach to estimate model uncertainty in semantic segmentation. During a mission, the semantic predictions and model uncertainty are used as input for terrain mapping. A key aspect of our pipeline is to link the mapped model uncertainty to a robotic planning objective based on active learning. This enables us to adaptively guide a UAV to gather the most informative terrain images to be labelled by a human for model training. Our experimental evaluation on real-world data shows the benefit of using our informative planning approach in comparison to static coverage paths in terms of maximising model performance and reducing labelling efforts.
