iSimLoc: 未見環境におけるシミュレーション画像を用いた視覚的グローバル位置推定
iSimLoc: Visual Global Localization for Previously Unseen Environments with Simulated Images
見た目や視点が大きく異なる環境でも、シミュレーション画像を活用した階層的な位置推定により、ドローンの視覚的グローバル自己位置推定を高速かつ高精度に実現する手法を提案。
著者: Peng Yin, Ivan Cisneros, Ji Zhang, Howie Choset, Sebastian Scherer
分類: cs.CV, cs.RO
原文アブストラクト
The visual camera is an attractive device in beyond visual line of sight (B-VLOS) drone operation, since they are low in size, weight, power, and cost, and can provide redundant modality to GPS failures. However, state-of-the-art visual localization algorithms are unable to match visual data that have a significantly different appearance due to illuminations or viewpoints. This paper presents iSimLoc, a condition/viewpoint consistent hierarchical global re-localization approach. The place features of iSimLoc can be utilized to search target images under changing appearances and viewpoints. Additionally, our hierarchical global re-localization module refines in a coarse-to-fine manner, allowing iSimLoc to perform a fast and accurate estimation. We evaluate our method on one dataset with appearance variations and one dataset that focuses on demonstrating large-scale matching over a long flight in complicated environments. On our two datasets, iSimLoc achieves 88.7\% and 83.8\% successful retrieval rates with 1.5s inferencing time, compared to 45.8% and 39.7% using the next best method. These results demonstrate robust localization in a range of environments.
