The first is the quickest growing segment of the vacuum cleaning market: robotic vacuum cleaners. Second is the newest weapon group in our militaries – air drones, ground robots for dealing with explosive devices, and underwater robots to map out what is going on in our oceans.
But there has also been a less obvious set of academic robot research going on – one that will impact many aspects of business. It is research which began over 20 years ago, in 1990, with a concept named Simultaneous Localization and Mapping (Slam). As small mobile robots were starting to be built by research robot makers, academics around the world began working on robots that could build maps from visual, sonar, laser range and other data sources.
Since the robots were mobile and didn’t know exactly where they were, the challenge was to simultaneously figure out the relative positions and orientations of a robot as it made different observations about its situation. This would be an easy task for a robot if it already had an accurate map to work with – but at the same time it was making decisions it was also having to build a picture of its surroundings.
Hence the word "simultaneous" in the name. Today, Slam algorithms are exceptionally good, and the sensors that can be used to collect data are now very low-cost.