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。此类系统大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环境中，基于静态数据集的预训练无法始终涵盖各类场景域，从而限制了机器人在任务执行期间的环境感知性能。近年来，自监督与全监督主动学习方法被提出以提升机器人视觉能力，但这些方法依赖于大规模域内预训练数据集或需要大量人工标注工作量。我们提出一种面向语义分割的半监督主动学习规划方法，相比全监督方法能够显著降低人工标注需求。该方法利用自适应地图引导的规划器，通过高模型不确定性驱动探索未勘测空间前沿区域，从而采集用于人工标注的训练数据。本方法的核心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，本方法在显著减少人工标注工作量的前提下，能达到接近全监督方法的分割性能，同时优于自监督方法。