Employing active contours and artificial neural networks in representing ultrasonic range data

Active snake contours and Kohonen's self-organizing feature maps (SOM) are considered for efficient representation and evaluation of the maps of an environment obtained with different ultrasonic arc map (UAM) processing techniques. The mapping results are compared with a reference map acquired with a very accurate laser system. Both approaches are convenient ways of representing and comparing the map points obtained with different techniques among themselves, as well as with an absolute reference. Snake curve fitting results in more accurate maps than SOM since it is more robust to outliers. The two methods are sufficiently general that they can be applied to discrete point maps acquired with other mapping techniques and other sensing modalities as well.