In the field of visual Place Recognition (vPR), sequence-based techniques have received close attention since they combine visual information from multiple measurements to enhance the results. This paper is concerned with the task of identifying sequence boundaries, corresponding to physical scene limits of the robot’s trajectory, that can potentially be re-encountered during an autonomous mission. In contrast to other vPR techniques that select a predefined length for all the image sequences, our approach focuses on a dynamic segmentation and allows for the visual information to be consistently grouped between different visits of the same area. To achieve this, we compute similarity measurements between consecutively acquired frames to incrementally formulate a similarity signal. Then, local extrema are detected in the Scale-Space domain regardless the velocity that a camera travels and perceives the world. Accounting for any detection inconsistencies, we explore asynchronous sequence-based techniques and a novel weighted temporal consistency scheme that strengthens the performance. Our dynamically computed sequence segmentation is tested on two different vPR methods offering an improvement in the systems’ accuracy.

在视觉位置识别（vPR）领域，基于序列的技术受到了广泛的关注，因为它们结合了来自多个测量的视觉信息以增强结果。本文涉及识别序列边界的任务，该序列边界对应于机器人轨迹的物理场景限制，在自动执行任务中可能会遇到。与为所有图像序列选择预定义长度的其他vPR技术相比，我们的方法着重于动态分割，并允许将视觉信息一致地分组在同一区域的不同访问之间。为了实现这一点，我们计算连续获取的帧之间的相似度测量值，以递增地表达相似度信号。然后，无论摄像机行进和感知世界的速度如何，都可以在Scale-Space域中检测到局部极值。考虑到任何检测到的不一致，我们探索了基于异步序列的技术和一种新颖的加权时间一致性方案，该方案可以增强性能。我们动态计算的序列分段已在两种不同的vPR方法上进行了测试，从而提高了系统的准确性。