Learning OpenCV

Afterword

We've covered a lot of theory and practice in this book, and we've described some of the plans for what comes next. Of course, as we're developing the software, the hardware is also changing. Cameras are now cheaper and have proliferated from cell phones to traffic lights. A group of manufacturers are aiming to develop cell-phone projectors—perfect for robots, because most cell phones are lightweight, low-energy devices whose circuits already include an embedded camera. This opens the way for close-range portable structured light and thereby accurate depth maps, which are just what we need for robot manipulation and 3D object scanning.

Both authors participated in creating the vision system for Stanley, Stanford's robot racer that won the 2005 DARPA Grand Challenge. In that effort, a vision system coupled with a laser range scanner worked flawlessly for the seven-hour desert road race [Dahlkamp06]. For us, this drove home the power of combining vision with other perception systems: the previously unsolved problem of reliable road perception was converted into a solvable engineering challenge by merging vision with other forms of perception. It is our hope that—by making vision easier to use and more accessible through this book—others can add vision to their own problem-solving tool kits and thus find new ways to solve important problems. That is, with commodity camera hardware and OpenCV, people can start solving real problems such as using stereo vision as an automobile ...