未知環境における意味セグメンテーションのための情報経路計画を用いた半教師あり能動学習
Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path Planning
ロボットが未知環境で意味セグメンテーションを学習する際、不確実性の高い未探索領域へ経路計画で誘導し、少数の高品質な人手ラベルと自動生成した擬似ラベルを組み合わせることで、人手ラベル作業を大幅に削減する半教師あり能動学習手法を提案した。
著者: Julius Rückin, Federico Magistri, Cyrill Stachniss, Marija Popović
分類: cs.RO, cs.LG
原文アブストラクト
Semantic segmentation enables robots to perceive and reason about their environments beyond geometry. Most of such systems build upon deep learning approaches. As autonomous robots are commonly deployed in initially unknown environments, pre-training on static datasets cannot always capture the variety of domains and limits the robot's perception performance during missions. Recently, self-supervised and fully supervised active learning methods emerged to improve a robot's vision. These approaches rely on large in-domain pre-training datasets or require substantial human labelling effort. We propose a planning method for semi-supervised active learning of semantic segmentation that substantially reduces human labelling requirements compared to fully supervised approaches. We leverage an adaptive map-based planner guided towards the frontiers of unexplored space with high model uncertainty collecting training data for human labelling. A key aspect of our approach is to combine the sparse high-quality human labels with pseudo labels automatically extracted from highly certain environment map areas. Experimental results show that our method reaches segmentation performance close to fully supervised approaches with drastically reduced human labelling effort while outperforming self-supervised approaches.
