Object Detection Using Region Growing In Laser Range Imagery

A technique for detecting objects in noisey range imagery and within a cluttered background is discussed and demonstrated on real data. The approach is based on a simple region growing technique which uses the range difference between neighboring pixels in the image as the similarity measure in the growing process. A region list is generated by the process which includes several region properties. Some of these properties are used to validate regions as detections based on a priori knowledge of general object structure. The performance of the techique is tested at several spacial resolutions and with ambiguous range data. Results of varying the similarity measure threshold is also shown. An approach to object orientation estimation is also discussed.