B. Douillard∗, N. Nourani-Vatani∗, M. Johnson-Roberson, O. Pizarro, S. Williams, C. Roman, I. Vaughn
(* co-first authors)
Autonomous Robots, Volume 35, Issue 4 , pp 255-269
A method for segmenting three-dimensional data of underwater unstructured terrains is presented. The three-dimensional point clouds are converted to two-dimensional elevation maps and analyzed for seg- mentation in the frequency domain. The lower frequency components represent the slower varying undulations of the underlying ground. The cut-off frequency, below which the frequency components form the ground sur- face, is determined automatically using peak detection. The user can also specify a maximum admissible size of objects to drive the automatic detection of the cut-off frequency. The points above the estimated ground sur- face are clustered via standard proximity clustering to form object segments. The precision of the segmenta- tion is compared against ground truth hand labelled data acquired by a stereo camera pair and a struc- tured light sensor. It is also evaluated for registration error when the extracted segments are used for sub- map alignment. The proposed approach is compared to three point cloud based and two image based segmen- tation algorithms. The results show that the approach is applicable to a range of different terrains and is able to generate features useful for navigation.