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.

翻译：语义分割使机器人能够超越几何层面感知和推理周围环境。此类系统多基于深度学习方法构建。由于自主机器人通常部署在初始未知的环境中，基于静态数据集的预训练难以涵盖所有领域变化，从而限制了机器人在任务中的感知性能。近年来，自监督和全监督的主动学习方法相继出现，旨在提升机器人的视觉能力。然而，这些方法依赖大规模领域内预训练数据集，或需要大量人工标注工作。我们提出了一种面向语义分割的半监督主动学习规划方法，相比全监督方法显著降低了人工标注需求。该方法利用自适应地图规划器，引导系统向未探索空间边界移动，同时结合高模型不确定性采集需要人工标注的训练数据。本方法的关键在于将稀疏的高质量人工标注与从高置信度环境地图区域自动提取的伪标签相结合。实验结果表明，本方法在人工标注工作量大幅减少的情况下，语义分割性能接近全监督方法，并优于自监督方法。