#event-based vision

Perhaps you remember my post some month ago about event-based particle filtering? In the meantime we extended the particle filter to a full SLAM algorithm. SLAM is the abbreviation for "Simultaneous Localization and Mapping" and refers to the fundamental problem in robotics and computer vision to generate a map of an unknown environment during navigation. Your intelligent vacuum cleaner or bring-me-beer-robot will use SLAM :)

Our approach to SLAM is highly efficient and my ad-hoc implementation runs already in 10x realtime. This makes our algorithm especially suitable for embedded systems.

I have been to Sankt-Petersburg in Russia last week and presented the event-based SLAM method at the conference ICVS 2013. Sankt-Petersburg is a crazy city. They are palaces and gold everywhere while at the same time most of the city is decaying...

For those more interested in science, a small remark about my recent scientific research. I published an interesting, new method for robot self-localization using a low-profile event-based sensor. See here and here for more information about the sensor.

The main challenge was to adapt well known methods in computer vision to a new kind of hardware. Instead of full images at a fixed frame rate our method works with a stream of individual pixel events. This has the advantage that much less computation power, almost no memory and much less electrical power is required - making our method especially suitable for embedded applications.

Next week I will fly to China and present the paper a the Conference on Robotics and Biomimetics - hooray! This will be an exciting trip and I am really curious what China is alike.