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信息感知和推理其环境。大多数此类系统基于深度学习方法。由于自主机器人通常部署在初始未知环境中，基于静态数据集的预训练无法始终涵盖多样的域，从而限制了机器人在任务期间的感知性能。近期，自监督和全监督主动学习方法被提出以改进机器人的视觉能力。这些方法依赖大规模域内预训练数据集或需要大量人工标注工作。我们提出了一种用于语义分割的半监督主动学习规划方法，该方法相较于全监督方法大幅降低了人工标注需求。我们利用一个自适应地图驱动的规划器，引导机器人向具有高模型不确定性的未探索空间前沿移动，以收集用于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签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，我们的方法在显著减少人工标注工作量的前提下，达到了接近全监督方法的分割性能，同时优于自监督方法。