Search Results for author: Tom Duckett

Found 8 papers, 2 papers with code

Towards Long-term Autonomy: A Perspective from Robot Learning

no code implementations24 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.

Localising Faster: Efficient and precise lidar-based robot localisation in large-scale environments

no code implementations4 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.

Robot Perception of Static and Dynamic Objects with an Autonomous Floor Scrubber

1 code implementation24 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.


Artificial Intelligence for Long-Term Robot Autonomy: A Survey

no code implementations13 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.

Recurrent-OctoMap: Learning State-based Map Refinement for Long-Term Semantic Mapping with 3D-Lidar Data

no code implementations2 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).

Learning monocular visual odometry with dense 3D mapping from dense 3D flow

no code implementations6 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.

Monocular Visual Odometry

3DOF Pedestrian Trajectory Prediction Learned from Long-Term Autonomous Mobile Robot Deployment Data

no code implementations30 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.

Decoder Human Detection +2

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