Diagnosing Deep SLAM for Domain-Shift Localization

In this study, we address a novel "domain-shift localization (DSL)" problem, by which "user robots" of a deep SLAM system localize a domain-shifted region in the robot workspace during their daily navigation. Furthermore, we present a case study pertaining to a simple deep SLAM system comprising a visual place classifier and visual odometry as exteroceptive and proprioceptive modules, respectively. Such a DSL method enables "mapper robots" to focus on available resources (e.g., time, energy, and computation) in the domain-shifted region, rather than the entire workspace, thereby significantly reducing the cost of per-domain DNN maintenance. Unlike conventional scenarios of SLAM diagnosis, the deep SLAM system comprises deep neural networks (DNNs) with black-box characteristics, which render it difficult to directly diagnose the internal signals of SLAM modules. Hence, we present a novel diagnosis algorithm that does not rely on internal signals but uses only the input/output signals of DNNs as input to DSL. Experiments demonstrate that, compared with a vanilla deep SLAM system that does not reflect fault diagnosis, the proposed deep SLAM framework can achieve a path that is more similar to the actual measured GPS path.