Posts Tagged: machine learning
We present a novel method for generating, predicting, and using Spatiotemporal Occupancy Grid Maps (SOGM), which embed future information of dynamic scenes.
Where can machine learning help robotic state estimation? That’s the question Prof. Tim Barfoot addressed in the November 11 edition of the Carnegie Mellon Robotics Institute Tartan SLAM series, a […]
The technology behind self-driving cars has been racing ahead – and as long as they are cruising along familiar streets, seeing familiar sights, they do very well. But the University of Toronto’s Florian Shkurti says that when driverless vehicles encounter something unexpected, all that progress can come screeching to a halt.
We present a self-supervised learning approach for the semantic segmentation of lidar frames. Our method is used to train a deep point cloud segmentation architecture without any human annotation.
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