異種センサ計測のマッチングのための深層位相相関ネットワーク
Deep Phase Correlation for End-to-End Heterogeneous Sensor Measurements Matching
姿勢誤差を微分可能な位相相関で逆伝播させ、異種センサ画像と地図画像を直接照合する軽量なエンドツーエンド学習手法を提案し、シミュレーションと航空・地上データセットで従来法を上回る精度を示した。
著者: Zexi Chen, Xuecheng Xu, Yue Wang, Rong Xiong
分類: cs.CV, cs.RO
原文アブストラクト
The crucial step for localization is to match the current observation to the map. When the two sensor modalities are significantly different, matching becomes challenging. In this paper, we present an end-to-end deep phase correlation network (DPCN) to match heterogeneous sensor measurements. In DPCN, the primary component is a differentiable correlation-based estimator that back-propagates the pose error to learnable feature extractors, which addresses the problem that there are no direct common features for supervision. Also, it eliminates the exhaustive evaluation in some previous methods, improving efficiency. With the interpretable modeling, the network is light-weighted and promising for better generalization. We evaluate the system on both the simulation data and Aero-Ground Dataset which consists of heterogeneous sensor images and aerial images acquired by satellites or aerial robots. The results show that our method is able to match the heterogeneous sensor measurements, outperforming the comparative traditional phase correlation and other learning-based methods. Code is available at https://github.com/jessychen1016/DPCN .
