no code implementations • 31 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.
1 code implementation • 4 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.
2 code implementations • 2 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.
1 code implementation • 17 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.
no code implementations • 3 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).
no code implementations • 28 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.