Search Results for author:
Found 7 papers, 5 papers with code
WildScenes: A Benchmark for 2D and 3D Semantic Segmentation in Large-scale Natural Environments
Recent progress in semantic scene understanding has primarily been enabled by the availability of semantically annotated bi-modal (camera and LiDAR) datasets in urban environments.
Pose-Graph Attentional Graph Neural Network for Lidar Place Recognition
This paper proposes a pose-graph attentional graph neural network, called P-GAT, which compares (key)nodes between sequential and non-sequential sub-graphs for place recognition tasks as opposed to a common frame-to-frame retrieval problem formulation currently implemented in SOTA place recognition methods.
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.
