Lifelong Change Detection: Continuous Domain Adaptation for Small Object Change Detection in Every Robot Navigation

The recently emerging research area in robotics, ground view change detection, suffers from its ill-posed-ness because of visual uncertainty combined with complex nonlinear perspective projection. To regularize the ill-posed-ness, the commonly applied supervised learning methods (e.g., CSCD-Net) rely on manually annotated high-quality object-class-specific priors. In this work, we consider general application domains where no manual annotation is available and present a fully self-supervised approach. The present approach adopts the powerful and versatile idea that object changes detected during everyday robot navigation can be reused as additional priors to improve future change detection tasks. Furthermore, a robustified framework is implemented and verified experimentally in a new challenging practical application scenario: ground-view small object change detection

A cross-correlation analysis of Pi oscillations in auroral luminosity and magnetic field variations

第6回極域科学シンポジウム[OS] 宙空圏11月16日（月）　国立極地研究所 2階 大会議