Active learning has recently gained attention in deep learning tasks dedicated to autonomous driving, such as image classification. However, semantic segmentation for point clouds remains a largely unexplored task in active learning, mainly due to the heavy computational cost of such work. In this paper, we present an analysis to reduce data redundancy in the large-scale dataset Semantic-Kitti, thanks to active learning uncertainty-based methods and data augmentation. We are able to demonstrate that data augmentation techniques is helping our active learning cycles, and achieve baseline accuracy with only 60% of the dataset.