  Point cloud-based large scale place recognition is an important but
challenging task for many applications such as Simultaneous Localization and
Mapping (SLAM). Taking the task as a point cloud retrieval problem, previous
methods have made delightful achievements. However, how to deal with
catastrophic collapse caused by rotation problems is still under-explored. In
this paper, to tackle the issue, we propose a novel Point Cloud-based
Rotation-aware Large Scale Place Recognition Network (RPR-Net). In particular,
to solve the problem, we propose to learn rotation-invariant features in three
steps. First, we design three kinds of novel Rotation-Invariant Features
(RIFs), which are low-level features that can hold the rotation-invariant
property. Second, using these RIFs, we design an attentive module to learn
rotation-invariant kernels. Third, we apply these kernels to previous point
cloud features to generate new features, which is the well-known SO(3) mapping
process. By doing so, high-level scene-specific rotation-invariant features can
be learned. We call the above process an Attentive Rotation-Invariant
Convolution (ARIConv). To achieve the place recognition goal, we build RPR-Net,
which takes ARIConv as a basic unit to construct a dense network architecture.
Then, powerful global descriptors used for retrieval-based place recognition
can be sufficiently extracted from RPR-Net. Experimental results on prevalent
datasets show that our method achieves comparable results to existing
state-of-the-art place recognition models and significantly outperforms other
rotation-invariant baseline models when solving rotation problems.
