Restricted access stuff

Autonomous vehicles (AVs) are used to perform routine tasks for industry, as well as being used in areas hazardous to humans. Machine vision can provide such vehicles with 'sight', allowing them to understand their surroundings and leading to more flexible use of AVs.

Detecting Obstructions

Stereo vision enables AVs to have a 3-dimensional (3D) understanding of their environment (Fig 1). This can be used to perform free space mapping (FSM). FSM allows an AV to find clear paths between obstacles. Firstly the ground plane (GP) is identified by fitting a plane through objects lying in it, e.g. floor markings. Edge detection identifies features in the AV's field of vision (Fig 2). Edges that are not in the GP are assumed to belong to objects that extend to the GP, and thus would obstruct the AV's movements (Fig 3). This approach enables 3D objects to be distinguished from features like floor markings. Three-dimensional scene edges, derived using stereovision, can also be used for vehicle navigation, and for the location and tracking of known objects (Fig 4).

Exploring New Surroundings

If an autonomous guided vehicle (AGV) enters an unknown area, it must be able to understand its surroundings, as it proceeds. One approach to achieving this, involves analysing the images received from a camera placed at the front of the AGV. Firstly, features such as corners (T-junctions or Y-junctions) are located as the AGV moves through the scene. Such features are chosen, because the point of intersection of the lines which form a corner is fixed regardless of the angle from which it is viewed. The features identified are then tracked in the series of images. As the AGV moves, the apparent motion of features in its field of view will depend on their distance. In fact from the trajectories of features in the image, their 3D positions, and the 3D motion of the AGV, can be estimated. By superimposing a Cartesian grid on the image (see Fig ), a driveable region can be defined.

AV Surveillance

AVs have mechanisms to locate their position relative to their environment. Correct calculation of the AV's position is essential for safe and effective performance of tasks. It is therefore necessary to monitor the movements of an AV and correlate this information with the AV's own estimate of its position. One such sytem uses four fixed cameras to survey a workspace. The image from each camera is used to identify objects by subtracting the received image from a reference image of the empty workspace. The positions of the cameras are calibrated so that the positions of the objects on the floor can be determined from their positions in the image. The data from the four cameras is fused to achieve precise location. Objects, such as people, can be distinguished from an AV by using models which describe characteristic features.