Sequence-based visual place recognition (sVPR) aims to match frame sequences
with frames stored in a reference map for localization. Existing methods
include sequence matching and sequence descriptor-based retrieval. The former
is based on the assumption of constant velocity, which is difficult to hold in
real scenarios and does not get rid of the intrinsic single frame descriptor
mismatch. The latter solves this problem by extracting a descriptor for the
whole sequence, but current sequence descriptors are only constructed by
feature aggregation of multi-frames, with no temporal information interaction.
In this paper, we propose a sequential descriptor extraction method to fuse
spatiotemporal information effectively and generate discriminative descriptors.
Specifically, similar features on the same frame focu on each other and learn
space structure, and the same local regions of different frames learn local
feature changes over time. And we use sliding windows to control the temporal
self-attention range and adpot relative position encoding to construct the
positional relationships between different features, which allows our
descriptor to capture the inherent dynamics in the frame sequence and local
feature motion.

本文提出了一种序列化描述符提取方法，以有效地融合时空信息并生成有区别的描述符，使用滑动窗口来控制时间自我关注范围，并采用相对位置编码来构建不同特征之间的位置关系，从而捕捉帧序列中的固有动态和局部特征运动。