Search Results for author: Milad Ramezani

Found 6 papers, 3 papers with code

Pose-Graph Attentional Graph Neural Network for Lidar Place Recognition

no code implementations31 Aug 2023 Milad Ramezani, Liang Wang, Joshua Knights, Zhibin Li, Pauline Pounds, Peyman Moghadam

This paper proposes a lidar place recognition approach, called P-GAT, to increase the receptive field between point clouds captured over time.

Domain Adaptation

Uncertainty-Aware Lidar Place Recognition in Novel Environments

1 code implementation4 Oct 2022 Keita Mason, Joshua Knights, Milad Ramezani, Peyman Moghadam, Dimity Miller

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

2 code implementations2 Mar 2022 Joshua Knights, Peyman Moghadam, Milad Ramezani, Sridha Sridharan, Clinton Fookes

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.

Incremental Learning

LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition

1 code implementation17 Sep 2021 Kavisha Vidanapathirana, Milad Ramezani, Peyman Moghadam, Sridha Sridharan, Clinton Fookes

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.

3D Place Recognition Retrieval +1

Online Estimation of Diameter at Breast Height (DBH) of Forest Trees Using a Handheld LiDAR

no code implementations3 Aug 2021 Alexander Proudman, Milad Ramezani, Maurice Fallon

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

no code implementations28 Jan 2020 Milad Ramezani, Georgi Tinchev, Egor Iuganov, Maurice Fallon

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.

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