no code implementations • 24 Dec 2022 • Zhi Yan, Li Sun, Tomas Krajnik, Tom Duckett, Nicola Bellotto
In the future, service robots are expected to be able to operate autonomously for long periods of time without human intervention.
1 code implementation • 23 Mar 2021 • Zhicheng Zhou, Cheng Zhao, Daniel Adolfsson, Songzhi Su, Yang Gao, Tom Duckett, Li Sun
Benefiting from the NDT representation and NDT-Transformer network, the learned global descriptors are enriched with both geometrical and contextual information.
no code implementations • 4 Mar 2020 • Li Sun, Daniel Adolfsson, Martin Magnusson, Henrik Andreasson, Ingmar Posner, Tom Duckett
More importantly, the Gaussian method (i. e. deep probabilistic localisation) and non-Gaussian method (i. e. MCL) can be integrated naturally via importance sampling.
1 code implementation • 24 Feb 2020 • Zhi Yan, Simon Schreiberhuber, Georg Halmetschlager, Tom Duckett, Markus Vincze, Nicola Bellotto
The proposed system is based on multiple sensors including 3D and 2D lidar, two RGB-D cameras and a stereo camera.
Robotics
no code implementations • 13 Jul 2018 • Lars Kunze, Nick Hawes, Tom Duckett, Marc Hanheide, Tomáš Krajník
Autonomous systems will play an essential role in many applications across diverse domains including space, marine, air, field, road, and service robotics.
no code implementations • 2 Jul 2018 • Li Sun, Zhi Yan, Anestis Zaganidis, Cheng Zhao, Tom Duckett
Most existing semantic mapping approaches focus on improving semantic understanding of single frames, rather than 3D refinement of semantic maps (i. e. fusing semantic observations).
no code implementations • 6 Mar 2018 • Cheng Zhao, Li Sun, Pulak Purkait, Tom Duckett, Rustam Stolkin
Dense 2D flow and a depth image are generated from monocular images by sub-networks, which are then used by a 3D flow associated layer in the L-VO network to generate dense 3D flow.
no code implementations • 30 Sep 2017 • Li Sun, Zhi Yan, Sergi Molina Mellado, Marc Hanheide, Tom Duckett
Our approach, T-Pose-LSTM (Temporal 3DOF-Pose Long-Short-Term Memory), is trained using long-term data from real-world robot deployments and aims to learn context-dependent (environment- and time-specific) human activities.