Simultaneous Localization and Mapping

Abstract

In this chapter a SLAM framework for AUVs equipped with an MSIS operating in manmade structured environments is proposed. In the previous chapter, the use of techniques such as the Hough transform and the Kalman filter were studied in the context of a localization problem. Here, these techniques are further explored for their application in SLAM. The proposed approach is composed of two parts running simultaneously. The first is a line feature extraction algorithm which is responsible for managing both the measurements arriving from the MSIS and the vehicle position estimates from the SLAM system to search continuously for new features by means of a voting scheme. Eventually, when a new feature is detected, the algorithm also estimates its uncertainty parameters through an analysis of the imprint left in the acoustic images. The second part is a Kalman filter implementation which is the core of the proposed SLAM system. This filter merges the information from various sensors (DVL, compass and pressure sensor) and the observations from the feature extraction algorithm in order to estimate the vehicle’s motion and to build and maintain a feature based map (see Figure 6.1 for a diagram of the complete system). In addition, the problems associated with large scenarios have also been addressed through the implementation of a local map building procedure. At the end of the chapter, two tests performed with real sensor data endorse the proposed SLAM approach. The first employs the dataset corresponding to the previously presented CIRS water tank test, while the second undertakes amore realistic application scenario with a dataset obtained in an abandoned marina.