We consider the problem of detecting, in the visual sensing data stream of an autonomous mobile robot, semantic patterns that are unusual (i.e., anomalous) with respect to the robot's previous experience in similar environments. These anomalies might indicate unforeseen hazards and, in scenarios where failure is costly, can be used to trigger an avoidance behavior. We contribute three novel image-based datasets acquired in robot exploration scenarios, comprising a total of more than 200k labeled frames, spanning various types of anomalies. On these datasets, we study the performance of an anomaly detection approach based on autoencoders operating at different scales.

翻译：我们认为,在自动移动机器人的视觉遥感数据流中,检测出与机器人以前在类似环境中的经历不同寻常的语义图案(即异常)的问题。这些异常现象可能表明未预见的危险,在失败代价高昂的情况下,可以用来引发避免行为。我们贡献了在机器人探索情景中获得的三个新的图像数据集,总共包括200多公里的标签框架,跨越了各种异常类型。在这些数据集中,我们研究了基于在不同尺度运行的自动编码器的异常探测方法的性能。