Author Archives: admin

Interstellar – Inspirational Science

What makes Interstellar’s black hole so visually spectacular? I explore the physics behind the film: http://leuvendiaries.blogspot.be/2015/02/interstellar-inspirational-science.html The black hole Gargantua, from the film Interstellar [source]

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The Real Risks of AI

“The development of full artificial intelligence could spell the end of the human race” – Stephen Hawking. “AI represents an existential threat to mankind” – Elon Musk. What is it about AI today, that is making leading scientists and engineers … Continue reading

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The Stixel World

A first implementation of detecting stixels from stereo data. Stixels, a compact representation of 3D space for robot obstacle avoidance and navigation. Introduced by Badino et. al., 2009, stixels are a method to detect the nearest obstacles in front of … Continue reading

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A Case Of the Missing Marbles

What better way to spend a rainy June afternoon than to wander through the halls of the British Museum, taking in 2500 year-old scenes, real and mythical, immortalized in marble? My report on the Elgin Marbles, here.

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Hong Kong Street Scenes

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ICRA 2014 Highlights Reel

Dragons, humanoids, flying, crawling and climbing robots from ICRA 2014.

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Going to Hong Kong!

Nghia and my paper, Localization on Freeways using the Horizon Line Signature, has been accepted for publication at the Workshop on Visual Place Recognition in Changing Environments, held at the International Conference of Robotics and Automation (ICRA 2014) in Hong … Continue reading

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Pedestrian Detection using Histogram of Gradients (HoG) and a Random Forests Classifier

This entry is part 1 of 1 in the series Pedestrian Detection

1,178 64×128 sized images of pedestrians (positive samples) and 4,530 of the same sized negative samples were extracted from the INRIA dataset and divided randomly into two to give a training set and a validation set. HoG features on the … Continue reading

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A First Attempt at Lane Detection

This entry is part 2 of 2 in the series Visual Localization Along Highways

Lane Detection after filtering the image with a filter that accentuates vertical lines of a certain width and then applying the Hough Transform. Passing the output of the Hough Transform through a Kalman Filter for temporal smoothing is expected to … Continue reading

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100 K: Visual Localization along Melbourne’s Highways at 100 km/hr

This entry is part 1 of 2 in the series Visual Localization Along Highways
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