Abstract
Range is a very important and useful physical property. We can extract most of the physical features of an object from a 3-D image. This thesis is about analysing range images taken from a low resolution laser radar system. The objective of this research is to locate and attempt to identify obstacles in the surroundings for an unmanned small tracked vehicle to find its way. A short range (less than 30 metres) laser radar range finder, provided by the Ministry of Defense, gathered range images around the vehicle. Trees, rocks and walls are classified as obstacles. Roads, grassland and bushes are classified as passable objects. In the cases where the objects cannot be identified, we use the steepness as a guideline to classify the object as obstacles or not. Simple image processing techniques are applied to analyse the range image and satisfactory results are obtained. Obstacles can be located in the range images. The images are first segmented by three methods. Firstly, the range gating method is applied which segments the images- according to the information in their range histograms. Secondly, the gradient thresholding method is applied which distinguishes the steep obstacles from the non-steep objects. Thirdly, the spatial isolation is applied which isolates each individual object. The only information contained in a range image is the three dimensions of the object, so we concentrated on the analysis of the physical properties. Besides the size and shape, the texture of an object can also be extracted. Texture reflects what type of objects we are looking at. Walls, plains and other flat objects have fine textures while trees and bushes have rough textures. We have investigated various textural properties derived from the co-occurrence matrix. Another important physical property is the gradient because high gradient always implies obstacles, and these are things which an un-manned vehicle must avoid. The classification method uses the distance function to classify objects. Finally, the algorithm is implemented on an array of transputers. Promising results were observed. By implementing the algorithm onto an array of transputers, the processing time was reduced, and the obstacles can be identified from the range images.