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

Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling ICRA 2021

LiDAR-based 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

Locus is a global descriptor for large-scale place recognition using sequential 3D LiDAR point clouds. It encodes topological relationships and temporal consistency of scene components to obtain a discriminative and view-point invariant scene representation.

 

Kavisha Vidanapathirana, Peyman Moghadam, Ben Harwood, Muming Zhao, Sridha Sridharan, and Clinton Fookes. “Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling.” IEEE International Conference on Robotics and Automation (ICRA), 2021. 

 

Download the full paper here.

Code repository: https://github.com/csiro-robotics/locus

For more information, contact  Dr Peyman Moghadam