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

Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling