Simultaneous localization and mapping (SLAM) is a technique used to determine the location of a mobile vehicle in an unknown environment, while constructing a map of the unknown environment at the same time. Mobile platforms, which make use of SLAM algorithms, have industrial applications in autonomous maintenance, such as the inspection of flaws and defects in oil pipelines and storage tanks. An important component of SLAM is feature extraction, which is the process of detecting and extracting significant features such as corners, edges, and walls in an environment. Here, the use of sonars as sensors mounted on a mobile platform is examined, and a comparison of different algorithms currently in use is made and presented. This comparison is performed through a combination of experimental and numerical studies. The triangulation-based fusion algorithm is examined for point feature detection, and the standard Hough Transform and the triangulation Hough fusion (THF) are used for line detection. Comparisons are discussed and presented along with ongoing work.

This content is only available via PDF.
You do not currently have access to this content.