# Stereo cameras

The stereo cameras approach is a method of distilling a noisy video signal into a coherent data set that a computer can begin to process into actionable symbolic objects, or abstractions. Stereo cameras is one of many approaches used in the broader fields of computer vision and machine vision.

## Calculation

In this approach, two cameras with a known physical relationship (i.e. a common field of view the cameras can see, and how far apart their focal points sit in physical space) are correlated via software. By finding mappings of common pixel values, and calculating how far apart these common areas reside in pixel space, a rough depth map can be created. This is very similar to how the human brain uses stereoscopic information from the eyes to gain depth cue information, i.e. how far apart any given object in the scene is from the viewer.

The camera attributes must be known, focal length and distance apart etc., and a calibration done. Once this is completed the systems can be used to sense the distances of objects by triangulation. Finding the same singular physical point in the two left and right images is known as the correspondence problem. Correctly locating the point gives the computer the capability to calculate the distance that the robot or camera is from the object. On the BH2 Lunar Rover the cameras use five steps: a bayer array filter, photometric consistency dense matching algorithm, a Laplace of Gaussian (LoG) edge detection algorithm, a stereo matching algorithm and finally uniqueness constraint.[1] A stereoscopic camera is the key part of stereo imaging. Because in daily life is very difficult to get to, ordinary people may be on the stereo camera feel unfamiliar, but in fact the technology has been around for a long time. As early as in ancient Greek times, Euclidean have been found, people around the eyes to see the scenery is different, this is the main reason people can gain insight into the three-dimensional space, in modern terms is binocular disparity (binocular parallax), which is the basic principle of stereo image.

## Uses

This type of stereoscopic image processing technique is used in applications such as robotic control and sensing, crowd dynamics monitoring and off-planet terrestrial rovers; for example, in mobile robot navigation, people tracking, gesture recognition, targeting, 3D surface visualization, immersive and interactive gaming.[2] Although the Xbox Kinect sensor is also able to create a depth map of an image, it uses an infrared camera for this purpose, and does not use the dual-camera technique.

Other approaches to stereoscopic sensing include time of flight sensors and ultrasound.

## References

1. ^ Ming Xie (2010). Ming Xie ... [et al.], ed. Intelligent robotics and applications : second international conference, ICIRA 2009, Singapore, December 16–18, 2009 : proceedings (Online-Ausg. ed.). Berlin: Springer. ISBN 978-3-642-10816-7. Retrieved 13 March 2011.
2. ^ "Technology Benefits". Focus Robotics. Retrieved 13 March 2011.