This thesis presents an inspection method based on RGB-D profiling for the rail industry. The proposed approach uses inexpensive RGB-D cameras to generate color and geometrical information of the observations, and stitches each consecutive scan from the sensor to form a map, provided that the two scans contain the information from the same observation. Using a technique known as pairwise registration, the errors between these consecutive scans are minimized using error minimization algorithms such as Iterative Closest Point and Normal Distributions Transform. Once the error between each consecutive scan is minimized, the scans are then converted into a global co-ordinate frame work to form a global map of all the added scans. The proposed approach could be used as a map-based identification technique by comparing the past global map to newly acquired scans while also reducing computation time effectively. The effectiveness of this approach is demonstrated by developing a system that uses multiple RGB-D cameras to detect railway defects such as spikes. The applicability of the proposed approach to other applications is then evaluated by profiling long lengths of road.