Fault-Diagnosing DCN-SLAM for 3D Change Object Detection: A Method based on Masking Input Images

Abstract

Although image change detection (ICD) methods provide good detection accuracy for many scenarios, most of the existing methods rely on place-specific background modeling. The time/space cost for such place-specific models becomes prohibitive for large-scale scenarios, such as long-term robotic visual simultaneous localization and mapping (SLAM). Therefore, we propose a novel ICD framework that is specifically tailored for long-term SLAM. This study is inspired by the multi-map-based SLAM framework, where N multiple localizers are capable of mutual diagnosis, thus not requiring any explicit background modeling/model. We extend this multi-map diagnosis approach toward a more generic single-map-based object-level diagnosis framework (i.e., ICD), where state-of-the-art self-localization systems can be used in their original form, which is as the change object indicator. The available single localizer is extended to different N localizers by introducing different N masked input images. Further, we also consider map diagnosis on a state-of-the-art deep-visual-SLAM system (rather than on conventional bag-of-words or landmark -based systems) in which the blackbox nature of the deep convolutional neural network (DCN) complicates the diagnosis problem. We also consider a 3D point cloud (PC) -based SLAM, and for the first time (to the best of our knowledge) adopt the state-of-the-art scan context PC descriptor for the purpose of map diagnosis.