Just don’t call it a robot! Genius, innovation, pragmatism and serendipity in building a successful company with an inventive technology to make a sought-after consumer product.
Our good friend, Clive Loughlin, editor of the UK’s Industrial Robot, has sent along another interview to share with our readers.
Remember the previous one? The Essential Interview: When Bill Townsend Met Burton Doo
Industrial Robot‘s Joanne Pransky, U.S. editor, and iRobot’s Paolo Pirjamian offer up a thoroughly engaging and insightful Q&A session that to goes to the heart of innovation and success in robotics.
We include it here in Robotics Business Review as a PDF link to download:
The Essential Interview: iRobot’s Paolo Pirjanian
Clive has made it available for download until March 31st.
Please enjoy the second of — hopefully — many more such interviews to come.
Here’s a brief sampling…
Pransky: Tell us about the technology you spear-headed at Evolution, highlighting key years, developments, partners, etc
Pirjanian: When we started Evolution Robotics in 2001, we realized that one of the first key building blocks for robotics was autonomous navigation. At that time, the state-of-the-art in the autonomous navigation field was mainly being driven by people like Sebastrian Thrun and others from Carnegie Mellon University who were using expensive laser rangefinders such as SICK, and developing that with simultaneous localization and mapping (SLAM), a probabilistic approach to building a map while figuring out its location.
Early lab results showed that this can be done, but with hardware costs of tens of thousands of dollars. At Evolution, we were focused on going after the consumer market, and even in the other markets, $10,000 was not a deployable solution. We therefore decided that we needed to find a much more cost-effective approach to solving this navigation problem, and my intuition for this solution was the use of cameras.
Cameras, which were just getting embedded into cell phones, cost less than a dollar in high volume, and we knew we could ride that curve of volume, compared to a $20,000 laser range finder.
The challenge, of course, was to develop algorithms that would be able to interpret the images properly to build a visual map of the environment, and then to use the map for the robot to know its location.
We started developing these algorithms and then I stumbled upon Professor David Lowe from the University of British Columbia who had developed and patented a visual recognition algorithm, known in the industry as the scale invariant feature transform (SIFT).
SIFT was a paradigm shift in the computer vision community, and although I saw this algorithm as rudimentary, I also saw promise in it.
Evolution immediately licensed the SIFT patent and we then incremented an algorithm to develop the vision pattern recognition technology and combined it with SLAM technology.
We called it visual simultaneous localization and mapping (vSLAM), a term we coined. The algorithm builds visual landmarks and these landmarks become the anchoring points into the real world. Whenever the robot sees these landmarks, it is able to recognize this position very accurately and figure out where it is…
See related: iRobot is an RBR50 company