变尺度变化检测的故障诊断SLAM
Fault-Diagnosing SLAM for Varying Scale Change Detection
摘要：
在本文中，我们提出了一种新的基于故障诊断（FD）的方法，用于检测图像变化，该方法可以检测不同子模块之间的不一致性（例如，自定位）的视觉SLAM。与经典的变化检测方法，如成对图像比较（PC）和异常检测（AD），既不需要记忆的每个地图图像，也不需要维护最新的特定于位置的异常检测器在FD方法。当将不同SLAM子模块并入FD中时遇到的重大挑战涉及处理已经改变的对象的变化尺度（例如，地板上出现小的危险障碍物）。为了解决这个问题，我们重新考虑的词袋（BoW）的图像表示，利用其最新进展的自我定位和变化检测。作为一个关键的优势，BoW图像表示可以通过简单地裁剪原始BoW图像来重新组织成任何不同的缩放比例。此外，我们建议将不同的自定位模块与具有不同区分度的强和弱BoW特征相结合，并将强和弱自定位之间的不一致视为变化的指标。实验验证了所提出的方法对FD伴/不伴AD和/或PC的有效性。
英文摘要：
In this paper, we present a new fault diagnosis (FD) -based approach for detection of imagery changes that can detect significant changes as inconsistencies between different sub-modules (e.g., self-localizaiton) of visual SLAM. Unlike classical change detection approaches such as pairwise image comparison (PC) and anomaly detection (AD), neither the memorization of each map image nor the maintenance of up-to-date place-specific anomaly detectors are required in this FD approach. A significant challenge that is encountered when incorporating different SLAM sub-modules into FD involves dealing with the varying scales of objects that have changed (e.g., the appearance of small dangerous obstacles on the floor). To address this issue, we reconsider the bag-of-words (BoW) image representation, by exploiting its recent advances in terms of self-localization and change detection. As a key advantage, BoW image representation can be reorganized into any different scaling by simply cropping the original BoW image. Furthermore, we propose to combine different self-localization modules with strong and weak BoW features with different discriminativity, and to treat inconsistency between strong and weak self-localization as an indicator of change. The efficacy of the proposed approach for FD with/without AD and/or PC was experimentally validated.
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