Visual place recognition algorithms trade off three key characteristics:
their storage footprint, their computational requirements, and their resultant
performance, often expressed in terms of recall rate. Significant prior work
has investigated highly compact place representations, sub-linear computational
scaling and sub-linear storage scaling techniques, but have always involved a
significant compromise in one or more of these regards, and have only been
demonstrated on relatively small datasets. In this paper we present a novel
place recognition system which enables for the first time the combination of
ultra-compact place representations, near sub-linear storage scaling and
extremely lightweight compute requirements. Our approach exploits the
inherently sequential nature of much spatial data in the robotics domain and
inverts the typical target criteria, through intentionally coarse scalar
quantization-based hashing that leads to more collisions but is resolved by
sequence-based matching. For the first time, we show how effective place
recognition rates can be achieved on a new very large 10 million place dataset,
requiring only 8 bytes of storage per place and 37K unitary operations to
achieve over 50% recall for matching a sequence of 100 frames, where a
conventional state-of-the-art approach both consumes 1300 times more compute
and fails catastrophically. We present analysis investigating the effectiveness
of our hashing overload approach under varying sizes of quantized vector
length, comparison of near miss matches with the actual match selections and
characterise the effect of variance re-scaling of data on quantization.

本研究提出了一种新的机器人空间数据可视化算法，实现了超紧凑的空间表示，接近次线性存储缩放和极轻量计算需求，并将散列过载方法应用于过程中，使得在匹配 100 帧序列时，每个空间定位点仅需 8 字节存储和 37k 个单元操作即可达到超过 50％的检索率