  Place Recognition enables the estimation of a globally consistent map and
trajectory by providing non-local constraints in Simultaneous Localisation and
Mapping (SLAM). This paper presents Locus, a novel place recognition method
using 3D LiDAR point clouds in large-scale environments. We propose a method
for extracting and encoding topological and temporal information related to
components in a scene and demonstrate how the inclusion of this auxiliary
information in place description leads to more robust and discriminative scene
representations. Second-order pooling along with a non-linear transform is used
to aggregate these multi-level features to generate a fixed-length global
descriptor, which is invariant to the permutation of input features. The
proposed method outperforms state-of-the-art methods on the KITTI dataset.
Furthermore, Locus is demonstrated to be robust across several challenging
situations such as occlusions and viewpoint changes in 3D LiDAR point clouds.
The open-source implementation is available at:
https://github.com/csiro-robotics/locus .
