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===GT-MilliNoise: Graph transformer for point-wise denoising of indoor millimetre-wave point clouds=== | ===GT-MilliNoise: Graph transformer for point-wise denoising of indoor millimetre-wave point clouds=== | ||
| − | [[File:millinoise-1.gif | | + | [[File:millinoise-1.gif |240px]] [[File:millinoise-2.gif|240px]] [[File:millinoise-3.gif|240px]] |
GT-MilliNoise is a learning-based denoiser for indoor millimetere-wave radars point cloud. We also released MilliNoise a dataset that collects mmWave radar point cloud and labels each point with a sub-millimetric accuracy. | GT-MilliNoise is a learning-based denoiser for indoor millimetere-wave radars point cloud. We also released MilliNoise a dataset that collects mmWave radar point cloud and labels each point with a sub-millimetric accuracy. | ||
[[GTMillinoise | Read more]] | [[GTMillinoise | Read more]] | ||
Autonomy and Perception are two key aspects of modern robotics. Our work spans both of these areas. In particular, we employ vision and radar technologies and apply them to the development of autonomous robots. Regarding autonomy, we use both classical and modern control methods, as well as reinforcement learning.
Improve RL sample efficiency with two new tools: Episodic Noise and Difficulty Manager.
Investigation on Safe RL algorithm performance on a realistic industrial robot (a Driveable Vertical Mast Lift).
In this work we designed a contrastive learning-based technique to translate mmWave radar point clouds to depth images with Point2Depth.
GT-MilliNoise is a learning-based denoiser for indoor millimetere-wave radars point cloud. We also released MilliNoise a dataset that collects mmWave radar point cloud and labels each point with a sub-millimetric accuracy.
Autonomy and Perception are two key aspects of modern robotics. Our work spans both of these areas. In particular, we employ vision and radar technologies and apply them to the development of autonomous robots. Regarding autonomy, we use both classical and modern control methods, as well as reinforcement learning.
Improve RL sample efficiency with two new tools: Episodic Noise and Difficulty Manager.
Investigation on Safe RL algorithm performance on a realistic industrial robot (a Driveable Vertical Mast Lift).
In this work we designed a contrastive learning-based technique to translate mmWave radar point clouds to depth images with Point2Depth.
GT-MilliNoise is a learning-based denoiser for indoor millimetere-wave radars point cloud. We also released MilliNoise a dataset that collects mmWave radar point cloud and labels each point with a sub-millimetric accuracy.