A novel semantic loop closure detection (SLCD) method is proposed in this article for visual simultaneous localization and mapping systems. SLCD aims to relieve the instance-level semantic inconsistency issue that arose from dynamic industrial scenes (e.g., autonomous driving in big cities). As the first step in this direction, SLCD fully exploits both low- and high-level video frame information, in a coarse-to-fine way. In SLCD, we adopt a convolutional neural network based object detection to acquire object information from the consecutive frames. Meanwhile, we perform a bag of visual words based similarity calculation to narrow the frames to coarse loop closure candidates. For these candidates, we perform an object matching on them to find their semantic inconsistency cases and remove involved semantic inconsistencies according to their cases. Then, we recalculate the similarity scores for these candidates. Finally, loop closures are determined by the similarity scores and a geometrical verification. Favorable performance of the proposed method is demonstrated by comparing it to other state-of-the-art methods using data from several public datasets and our new Dynamic Scenes dataset.

中文翻译：

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动态工业场景中的语义闭环检测和实例级不一致消除


本文提出了一种新颖的语义闭环检测（SLCD）方法，用于视觉同步定位和建图系统。 SLCD旨在缓解动态工业场景（例如大城市的自动驾驶）引起的实例级语义不一致问题。作为朝这个方向迈出的第一步，SLCD 以由粗到细的方式充分利用低级和高级视频帧信息。在SLCD中，我们采用基于卷积神经网络的目标检测来从连续帧中获取目标信息。同时，我们执行基于视觉词袋的相似性计算，以将框架缩小到粗略的闭环候选者。对于这些候选者，我们对他们进行对象匹配，以找到他们的语义不一致情况，并根据他们的情况删除涉及的语义不一致情况。然后，我们重新计算这些候选者的相似度分数。最后，通过相似性得分和几何验证来确定闭环。通过使用来自多个公共数据集和我们新的动态场景数据集的数据与其他最先进的方法进行比较，证明了所提出的方法的良好性能。