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Found 6 papers, 3 papers with code
Pose-Graph Attentional Graph Neural Network for Lidar Place Recognition
This paper proposes a lidar place recognition approach, called P-GAT, to increase the receptive field between point clouds captured over time.
Uncertainty-Aware Lidar Place Recognition in Novel Environments
State-of-the-art lidar place recognition models exhibit unreliable performance when tested on environments different from their training dataset, which limits their use in complex and evolving environments.
InCloud: Incremental Learning for Point Cloud Place Recognition
In this paper we address the problem of incremental learning for point cloud place recognition and introduce InCloud, a structure-aware distillation-based approach which preserves the higher-order structure of the network's embedding space.
LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition
Experiments on two large-scale public benchmarks (KITTI and MulRan) show that our method achieves mean $F1_{max}$ scores of $0. 939$ and $0. 968$ on KITTI and MulRan respectively, achieving state-of-the-art performance while operating in near real-time.
Online Estimation of Diameter at Breast Height (DBH) of Forest Trees Using a Handheld LiDAR
While mobile LiDAR sensors are increasingly used to scan in ecology and forestry applications, reconstruction and characterisation are typically carried out offline (to the best of our knowledge).
Online LiDAR-SLAM for Legged Robots with Robust Registration and Deep-Learned Loop Closure
The efficiency of our method comes from carefully designing the network architecture to minimize the number of parameters such that this deep learning method can be deployed in real-time using only the CPU of a legged robot, a major contribution of this work.
