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This work covers the achievement and processing of three-dimensional data
from a stereo sensor, with the main objective to develop a perception mechanism to support 
driver assistance systems. The process begins with camera calibration, 
to obtain the point cloud of best possible quality to favour the further processing,
whose initial step is based on filtering the point cloud to reduce
noise and down sample the data.
The next step is the determination of the set of points that represent
the ground in the point cloud, so it is possible to estimate the navigable area of the vehicle
and consequently the number of obstacles and location within that area.
The purpose is to do the clustering of the various obstacles within the navigable area relatively to the ground plane
which was done using two different methods: the first approach involves the application of 
Euclidean cluster segmentation on stereo point cloud, and the second consists of the
implementation of a region growing algorithm applied on two-dimensional segmented data from laser scans. 
The methods performed are tested under real conditions and failure situations,
advantages and drawbacks of their applications are analysed and compared.


