Dynamic sonar perception

Thesis (Ph. D. in Marine Robotics)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 2003.Includes bibliographical references (leaves 183-192).Reliable sonar perception is a prerequisite of marine robot feature-based navigation. The robot must be able to track, model, map, and recognize aspects of the underwater landscape without a priori knowledge. This thesis explores the tracking and mapping problems from the standpoint of observability. The first part of the thesis addresses observability in mapping and navigation. Features are often only partially observable from a single vantage point; consequently, they must be mapped from multiple vantage points. Measurement/feature correspondences may only be observable after a lag, and feature updates must occur after a delay. A framework is developed to incorporate temporally separated measurements such that the relevant quantities are observable. The second part of the thesis addresses observability in tracking. Although there may be insufficient information from a single measurement to estimate the state of a target, there may be enough information to observe correspondences. The minimum information necessary for a dynamic observer to track locally curved targets is derived, and the computational complexity is determined as a function of sonar design, robot dynamics, and sonar configuration. Experimental results demonstrating concurrent mapping and localization (CML) using this approach to early sonar perception are presented, including results from an ocean autonomous underwater vehicle (AUV) using a synthetic aperture sonar at the GOATS 2002 experiment in Italy.Richard J. Rikoski.Ph.D.in Marine Robotic

Delayed stochastic mapping

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 2001.Includes bibliographical references (leaves 70-73).by Richard J. Rikoski.S.M

Towards constant-time SLAM on an autonomous underwater vehicle using synthetic aperture sonar

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.

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In this paper we present a technique for mapping partially observable features from multiple uncertain vantage points. The problem of concurrent mapping and localization (CML) is stated as follows. Starting from an initial known position, a mobile robot travels through a sequence of positions, obtaining a set of sensor measurements at each position. The goal is to process the sensor data to produce an estimate of the trajectory of the robot while concurrently building a map of the environment. In this paper, we describe a generalized framework for CML that incorporates temporal as well as spatial correlations. The representation is expanded to incorporate past vehicle positions in the state vector. Estimates of the correlations between current and previous vehicle states are explicitly maintained. This enables the consistent initialization of map features using data from multiple time steps. Updates to the map and the vehicle trajectory can also b

Stochastic Mapping Frameworks

Stochastic mapping is an approach to the concurrent mapping and localization (CML) problem. The approach is powerful because feature and robot states are explicitly correlated. Improving the estimate of any state automatically improves the estimates of correlated states. This paper describes a number of extensions to the stochastic mapping framework, which are made possible by the incorporation of past vehicle states into the state vector to explicitly represent the robot's trajectory. Having access to past robot states simplifies mapping, navigation, and cooperation. Experimental results using sonar data are presented

Towards Robust Data Association and Feature Modeling for Concurrent Mapping and Localization

One of the most challenging aspects of concurrent mapping and localization (CML) is the problem of data association. Because of uncertainty in the origins of sensor measurements, it is difficult to determine the correspondence between measured data and features of the scene or object being observed, while rejecting spurious measurements. However, there are many important applications of mobile robots where maps need to be built of complex environments, consisting of composite features, from noisy sensor data. This paper reviews several new approaches to data association and feature modeling for CML that share the common theme of combining information from multiple uncertain vantage points while rejecting spurious data. Our results include: (1) feature-based mapping from laser data using robust segmentation, (2) map-building with sonar data using a novel application of the Hough transform for perception grouping, and (3) a new stochastic framework for making delayed decisions for combination of data from multiple uncertain vantage points. Experimental results are shown for CML using laser and sonar data from a B21 mobile robot