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• W3108681405 abstract "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: this https URL ." @default.
• W3108681405 created "2020-12-07" @default.
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• W3108681405 date "2020-11-30" @default.
• W3108681405 modified "2023-09-22" @default.
• W3108681405 title "Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling." @default.
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