This paper applies a new constant-time, consistent and convergent Simultaneous Localization and Mapping (SLAM) algorithm to an autonomous underwater vehicle (AUV). A constant-time SLAM algorithm offers computation independent of workspace size and is one key component in the development of truly autonomous agents. The real-time deployment of such a system would be a landmark achievement for the mobile robotics community. This paper describes progress towards this goal focusing on the sub-sea domain — an area set to benefit massively from the autonomy afforded by SLAM. The primary sensor used in this work is a sixteen element synthetic aperture sonar (SAS) carried on the nose of the AUV “Caribou”. Using a novel target detection strategy, data gathered from a 40 minute survey is processed by the new SLAM algorithm and the results compared to both a ground truth and the quadratic time “gold standard ” full covariance SLAM algorithm. 1

Abstract — Tracking sonar features in real time on an underwater robot is a challenging task. One reason is the low observability of the sonar in some directions. For example, using a blazed array sonar one observes range and the angle to the array axis with fair precision. The angle around the axis is poorly constrained. This situation is problematic for tracking features in world frame Cartesian coordinates as the error surfaces will not be ellipsoids. Thus Gaussian tracking of the features will not work properly. The situation is similar to the problem of tracking features in camera images. There the unconstrained direction is depth and its errors are highly non-Gaussian. We propose a solution to the sonar problem that is analogous to the successful inverse depth feature parameterization for vision tracking, introduced by [1]. We parameterize the features by the robot pose where it was first seen and the range/bearing from that pose. Thus the 3D features have 9 parameters that specify their world coordinates. We use a nonlinear transformation on the poorly observed bearing angle to give a more accurate Gaussian approximation to the uncertainty. These features are tracked in a SLAM framework until there is enough information to initialize world frame Cartesian coordinates for them. The more compact representation can then be used for a global SLAM or localization purposes. We present results for a system running real time underwater SLAM/localization. These results show that the parameterization leads to greater consistency in the feature location estimates. I.

Abstract. This paper describes a new technique for tracking locally curved unknown objects using sonar. The approach explicitly accounts for relevant robot dynamics. Objects are tracked by looking for temporal sequences of observations that fit a kinematic model. The method is illustrated using data from a synthetic aperture sonar gathered by a BPAUV at NATO SACLANT’s GOATS 2002 experiment in the Ligurian Sea. 1 Introduction and State-of-Art To navigate relative to features, avoid obstacles, recognize objects, and interact with or manipulate the underwater environment, substantial improvements in sonar perception need to be made. Numerous groups have investigated sonar perception. Leonard and Durrant-