An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping

TL;DR

This paper presents a UAV planning framework that autonomously collects informative images for semantic segmentation model re-training, reducing labeling effort and improving model performance in aerial mapping tasks.

Contribution

It introduces a novel probabilistic terrain map-based planning framework that fuses multiple acquisition functions for adaptive UAV image collection.

Findings

Maximizes model performance with fewer labeled images

Outperforms state-of-the-art local planning methods

Proven effective on real-world and simulated data

Abstract

Unmanned aerial vehicles (UAVs) are frequently used for aerial mapping and general monitoring tasks. Recent progress in deep learning enabled automated semantic segmentation of imagery to facilitate the interpretation of large-scale complex environments. Commonly used supervised deep learning for segmentation relies on large amounts of pixel-wise labelled data, which is tedious and costly to annotate. The domain-specific visual appearance of aerial environments often prevents the usage of models pre-trained on publicly available datasets. To address this, we propose a novel general planning framework for UAVs to autonomously acquire informative training images for model re-training. We leverage multiple acquisition functions and fuse them into probabilistic terrain maps. Our framework combines the mapped acquisition function information into the UAV's planning objectives. In this way, the UAV adaptively acquires informative aerial images to be manually labelled for model re-training. Experimental results on real-world data and in a photorealistic simulation show that our framework maximises model performance and drastically reduces labelling efforts. Our map-based planners outperform state-of-the-art local planning.