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.

使用自适应地图规划的半监督主动学习方法，结合高质量的人工标签和自动提取的伪标签，显著降低人工标注的需求，从而在减少人工标注工作量的同时，达到接近全监督方法的分割性能，超过自监督方法。