UAVのための適応的経路計画によるマルチ解像度セマンティックセグメンテーション
Adaptive Path Planning for UAV-based Multi-Resolution Semantic Segmentation
UAVが撮影画像から細部が必要な領域を検出し、その場所だけ低高度で高解像度撮影するよう経路をオンラインで適応させる手法を提案。深層学習セグメンテーション精度と飛行高度の関係モデルを導入し、農地の作物/雑草分類で実データ評価した。
著者: Felix Stache, Jonas Westheider, Federico Magistri, Marija Popović, Cyrill Stachniss
分類: cs.RO, cs.AI
原文アブストラクト
In this paper, we address the problem of adaptive path planning for accurate semantic segmentation of terrain using unmanned aerial vehicles (UAVs). The usage of UAVs for terrain monitoring and remote sensing is rapidly gaining momentum due to their high mobility, low cost, and flexible deployment. However, a key challenge is planning missions to maximize the value of acquired data in large environments given flight time limitations. To address this, we propose an online planning algorithm which adapts the UAV paths to obtain high-resolution semantic segmentations necessary in areas on the terrain with fine details as they are detected in incoming images. This enables us to perform close inspections at low altitudes only where required, without wasting energy on exhaustive mapping at maximum resolution. A key feature of our approach is a new accuracy model for deep learning-based architectures that captures the relationship between UAV altitude and semantic segmentation accuracy. We evaluate our approach on the application of crop/weed segmentation in precision agriculture using real-world field data.
