セマンティックセグメンテーションとパターンマッチングを用いた航空地図ベースナビゲーション
Aerial Map-Based Navigation Using Semantic Segmentation and Pattern Matching
航空画像と地図データベース間でラベル同士のマッチングを行い、深層学習による物体ラベリングとパターンマッチングで絶対水平位置を推定する地図ベース航法を提案した論文。
著者: Youngjoo Kim
分類: cs.CV, cs.RO
原文アブストラクト
This paper proposes a novel approach to map-based navigation system for unmanned aircraft. The proposed system attempts label-to-label matching, not image-to-image matching, between aerial images and a map database. The ground objects can be labelled by deep learning approaches and the configuration of the objects is used to find the corresponding location in the map database. The use of the deep learning technique as a tool for extracting high-level features reduces the image-based localization problem to a pattern matching problem. This paper proposes a pattern matching algorithm that does not require altitude information or a camera model to estimate the absolute horizontal position. The feasibility analysis with simulated images shows the proposed map-based navigation can be realized with the proposed pattern matching algorithm and it is able to provide positions given the labelled objects.