Evaluation of an efficient approach for target tracking from acoustic imagery for the perception system of an autonomous underwater vehicle

Abstract

This article describes the core algorithms of the perception system to be included within an autonomous underwater vehicle (AUV). This perception system is based on the acoustic data acquired from a side scan sonar (SSS). These data should be processed in an efficient time, so as to the perception system could detect and recognize a predefined target. This detection and recognition outcome is then an important piece of knowledge for the AUV's dynamic mission planner (DMP). Effectively, the DMP should propose different trajectories, navigation depths and other parameters that will change the robot's behavior, according to the perception system output. Hence, the time to make a decision is critical to assure safe robot operation and to acquire good quality data, and consequently the efficiency of the on-line image processing from acoustic data, is a key issue. Current techniques for acoustic data processing are time and computationally intensive. Hence, it was decided to process data coming from a SSS using a technique that is used for radars, due to its efficiency and its amenability to on-line processing. The engineering problem to solve in this case, is underwater pipeline tracking for routinely inspections in the off-shore industry. Then, an automatic oil pipeline detection system was developed borrowing techniques from processing of radar measurements. The radar technique is known as Cell Average-Constant False Alarm Rate (CA-CFAR). With a slight variation of the algorithms underlying this radar technique, consisting of the previous accumulation of partial sums, a great improvement in computing time and effort is achieved. Finally, a comparison with previous approaches over images acquired with a SSS from a vessel in the Salvador de Bahia bay in Brazil, showed the feasibility of using this on-board technique for AUV perception.