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There are three key technological questions that must be answered to design autonomous vehicles: where am I, what's around me and what do I do next. SegNet addresses the second question, while a separate but complementary system answers the first by using images to determine both precise location and orientation.
The localisation system designed by Kendall and Cipolla runs on a similar architecture to SegNet, and is able to localise a user and determine their orientation from a single colour image in a busy urban scene. The system is far more accurate than GPS and works in places where GPS does not, such as indoors, in tunnels, or in cities where a reliable GPS signal is not available.
It has been tested along a kilometre-long stretch of King's Parade in central Cambridge, and it is able to determine both location and orientation within a few metres and a few degrees, which is far more accurate than GPS -- a vital consideration for driverless cars. Users can try out the system for themselves here.
The localisation system uses the geometry of a scene to learn its precise location, and is able to determine, for example, whether it is looking at the east or west side of a building, even if the two sides appear identical.
"Work in the field of artificial intelligence and robotics has really taken off in the past few years," said Kendall. "But what's cool about our group is that we've developed technology that uses deep learning to determine where you are and what's around you - this is the first time this has been done using deep learning."
"In the short term, we're more likely to see this sort of system on a domestic robot - such as a robotic vacuum cleaner, for instance," said Cipolla. "It will take time before drivers can fully trust an autonomous car, but the more effective and accurate we can make these technologies, the closer we are to the widespread adoption of driverless cars and other types of autonomous robotics."
The researchers are presenting details of the two technologies at the International Conference on Computer Vision in Santiago, Chile.
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