If we say that technology makes things very easy and provides the ability to think about and implement them then no one will question that because it is undoubtedly true.
One of the examples of rising technology is artificial vision; i.e., vision with the aid of computers. The scientists have navigated a way to detect anything with the help of machine by using computers. This is called obstacle detection.
Using obstacle detection, we check if the space is free of obstacles so that we can cross it safely. Since mobile Robots are now becoming so common, obstacle detection is very essential so that we can help in the functioning of these mobile devices.
In previous years, we commonly used digital cameras, scanners and SONAR to perform obstacle detection. These are actually the input devices which contribute to the collection of data. The data we have collected using these devices goes for processing by computers and hence used in obstacle detection.
But these are not efficient, as the input given by them is very limited. For Good Obstacle detection we need lots of data so that we can use it in processing and can perform efficient obstacle detection. The design of a good obstacle detection system should fulfill the following point. Our obstacle detecting system should:
- Detect correct obstacles; detect obstacles in the surroundings in satisfactory time.
- Also has the capability to identify and ignore the ground stuffs which appear like obstacles but not actually are.
We can perform optical detection using planar Homography and Image warping, Image segmentation and optical detection using epipolar geometry, planar homography and edge detection. In this section we will become familiar with these terms. Let's start with the planes.
Planes are used in obstacle detection because they are so simple to use. The transformation between a world plane and its corresponding image plane is known as Homography. To detect planar homographs we use a Random Sample Consensus algorithm.
It is a kind of algorithm in which image points of a common plane must satisfy the constraints. Lets have a look on the basic RANSAC algorithm.
Basic Ransac Scheme
In the basic RANSAC, first of all the corners are detected in both of the images. After this on these corners we will have to apply variance normalized correlation to see the matches.
From those selected points we select some points to compute Homography.
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