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.

翻译：近期在机器人领域兴起的地面视角变化检测问题，由于视觉不确定性结合复杂的非线性透视投影而存在病态性。为规整该病态性，常用的监督学习方法（如CSCD-Net）依赖人工标注的高质量物体类别先验知识。本文考虑无人工标注的通用应用领域，提出了一种完全自监督的方法。该方法采用了强大而通用的思想：在日常机器人导航中检测到的物体变化可作为额外先验知识，用于改进未来的变化检测任务。此外，本文实现了一种鲁棒化框架，并在新的具有挑战性的实际应用场景——地面视角小物体变化检测中进行了实验验证。