The robotic arm of TrimBot2020 at work on a bush!
On 27-29 September 2017, we met in Renningen at the Bosch Campus to discuss the advancements of our work. Here some shots of the three days.
We present an algorithm that exploits both the underlying 3D structure and image entropy to generate an adaptive matching window.
The TrimBot2020 project was mentioned in an article on Horizon: the EU Research & Innovation magazine. Here an extract of the article, which you can read here. “It’s a similar story for the robotic hedge trimmer being developed by a separate group of researchers. All the farmer or groundskeeper needs to do is mark which hedge needs trimming. ‘The user will sketch the garden, though not too accurately,’ said Bob Fisher, computer vision scientist at Edinburgh University, UK, and coordinator…
In this paper we present an algorithm which recovers the rigid transformation that describes the displacement of a binocular stereo rig in a scene, and uses this to include a third image to perform dense trinocular stereo matching and reduce some of the ambiguities inherent to binocular stereo.
Check out what the TrimBot2020 sees from its new 10 stereo camera rig
FlowNet 2.0 is the first optical flow approach based on deep learning that reaches state-of-the-art accuracy. At the same time it is by a factor 100 faster than previous state-of-the-art techniques. This allows for reliable motion estimation at interactive frame rates. For more information visit the paper page.