  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.
