Search Results for author: Milad Ramezani

Found 4 papers, 1 papers with code

InCloud: Incremental Learning for Point Cloud Place Recognition

no 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.

Simultaneous Localization and Mapping

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.

Legged Robots

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