233 citations · 423 across the 25 of their papers we have counts for
5 papers · 1 filter
Wild-Places: A Large-Scale Dataset for Lidar Place Recognition in Unstructured Natural Environments
Joshua Knights, Kavisha Vidanapathirana, Milad Ramezani +3
Many existing datasets for lidar place recognition are solely representative of structured urban environments, and have recently been saturated in performance by deep learning base…
Uncertainty-Aware Lidar Place Recognition in Novel Environments
Keita Mason, Joshua Knights, Milad Ramezani +2
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…
A Letter on Progress Made on Husky Carbon: A Legged-Aerial, Multi-modal Platform
Adarsh Salagame, Shoghair Manjikian, Chenghao Wang +8
Animals, such as birds, widely use multi-modal locomotion by combining legged and aerial mobility with dominant inertial effects. The robotic biomimicry of this multi-modal locomot…
Wildcat: Online Continuous-Time 3D Lidar-Inertial SLAM
Milad Ramezani, Kasra Khosoussi, Gavin Catt +5
We present Wildcat, a novel online 3D lidar-inertial SLAM system with exceptional versatility and robustness. At its core, Wildcat combines a robust real-time lidar-inertial odomet…
InCloud: Incremental Learning for Point Cloud Place Recognition
Joshua Knights, Peyman Moghadam, Milad Ramezani +2
Place recognition is a fundamental component of robotics, and has seen tremendous improvements through the use of deep learning models in recent years. Networks can experience sign…