This topic enlightens about the task of obtaining a set of points which when congregated together results in a “map”. These set of points which are obtained are basically the distances from the target object and they are obtained by using the common perception sensors like laser range finders and sonars.
Implementing this above mentioned “map” in these robots, they will be able to pinpoint each and every point in the map and also move to that particular location thereby making them autonomous or in other words self-governed. The reason for this is that, as stated before the map is a set of points which define the distance between the autonomous vehicle and the target. The advantage of this is that these robots need not be programmed each and every time to move to a particular location but instead can be programmed to only close the distance between the target and the autonomous vehicle ( that is if the function of the vehicle is to move to the target pinpointed by the user or programmer in the map ) again by using these perception sensors which are installed on the autonomous vehicle.
Also this topic throws light on the disadvantages of using the conventional cameras which are installed on the robots due to reasons like loss of “depth in vision “ of the and images captured by the camera, and other views like light being a dependant resource for the camera.
This topic also discusses about the main two types of visionary systems that are being used for mapping and positioning; their advantages and disadvantages over the each other and their basic working along with their applications prior to the advantages over each other.