DeepTIO：視覚ハルシネーションを組み込んだ熱慣性オドメトリ
DeepTIO: A Deep Thermal-Inertial Odometry with Visual Hallucination
熱画像から疑似視覚特徴を生成するハルシネーションネットワークと選択的融合を用い、煙や暗闇でもロバストに自己位置推定する熱慣性オドメトリ手法を提案した。
著者: Muhamad Risqi U. Saputra, Pedro P. B. de Gusmao, Chris Xiaoxuan Lu, Yasin Almalioglu, Stefano Rosa, Changhao Chen, Johan Wahlström, Wei Wang, Andrew Markham, Niki Trigoni
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
Visual odometry shows excellent performance in a wide range of environments. However, in visually-denied scenarios (e.g. heavy smoke or darkness), pose estimates degrade or even fail. Thermal cameras are commonly used for perception and inspection when the environment has low visibility. However, their use in odometry estimation is hampered by the lack of robust visual features. In part, this is as a result of the sensor measuring the ambient temperature profile rather than scene appearance and geometry. To overcome this issue, we propose a Deep Neural Network model for thermal-inertial odometry (DeepTIO) by incorporating a visual hallucination network to provide the thermal network with complementary information. The hallucination network is taught to predict fake visual features from thermal images by using Huber loss. We also employ selective fusion to attentively fuse the features from three different modalities, i.e thermal, hallucination, and inertial features. Extensive experiments are performed in hand-held and mobile robot data in benign and smoke-filled environments, showing the efficacy of the proposed model.
