Place Recognition is a crucial capability for mobile robot localization and
navigation. Image-based or Visual Place Recognition (VPR) is a challenging
problem as scene appearance and camera viewpoint can change significantly when
places are revisited. Recent VPR methods based on ``sequential
representations'' have shown promising results as compared to traditional
sequence score aggregation or single image based techniques. In parallel to
these endeavors, 3D point clouds based place recognition is also being explored
following the advances in deep learning based point cloud processing. However,
a key question remains: is an explicit 3D structure based place representation
always superior to an implicit ``spatial'' representation based on sequence of
RGB images which can inherently learn scene structure. In this extended
abstract, we attempt to compare these two types of methods by considering a
similar ``metric span'' to represent places. We compare a 3D point cloud based
method (PointNetVLAD) with image sequence based methods (SeqNet and others) and
showcase that image sequence based techniques approach, and can even surpass,
the performance achieved by point cloud based methods for a given metric span.
These performance variations can be attributed to differences in data richness
of input sensors as well as data accumulation strategies for a mobile robot.
While a perfect apple-to-apple comparison may not be feasible for these two
different modalities, the presented comparison takes a step in the direction of
answering deeper questions regarding spatial representations, relevant to
several applications like Autonomous Driving and Augmented/Virtual Reality.
Source code available publicly this https URL

比较三维点云方法和基于图像序列的方法在场所识别性能上的差异，结果表明对于特定度量跨度，基于图像序列的技术可以接近甚至超过基于点云的方法的表现。