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Robert Mandelbaum

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5 papers

1 author row

ICRA Conference 2002 Conference Paper

Sensor Fusion of Structure-from-Motion, Bathymetric 3D, and Beacon-Based Navigation Modalities

Hanumant Singh
Garbis Salgian
Ryan M. Eustice
Robert Mandelbaum

Describes an approach for the fusion of 3D data underwater obtained from multiple sensing modalities. In particular, we examine the combination of image-based structure-from-motion (SFM) data with bathymetric data obtained using pencil-beam underwater sonar, in order to recover the shape of the seabed terrain. We also combine image-based egomotion estimation with acoustic-based and inertial navigation data on board the underwater vehicle. When fusion is performed at the data level, each modality is used to extract 3D information independently. The 3D representations are then aligned and compared. In this case, we use the bathymetric data as ground truth to measure the accuracy and drift of the SFM approach. Similarly we use the navigation data as ground truth against which we measure the accuracy of the image-based ego-motion estimation. We examine how low-resolution bathymetric data can be used to seed the higher-resolution SFM algorithm, improving convergence rates, and reducing drift error. Similarly, acoustic-based and inertial navigation data improves the convergence and drift properties of egomotion estimation.

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ICRA Conference 2000 Conference Paper

Terrain Reconstruction for Ground and Underwater Robots

Robert Mandelbaum
Garbis Salgian
Harpreet S. Sawhney
Michael W. Hansen

We describe a new image-processing algorithm for estimating both the egomotion of an outdoor robotic platform and the structure of the surrounding terrain. The algorithm is based on correlation, and is embedded in an iterative, multi-resolution framework. As such, it is suited to outdoor ground-based and underwater scenes. Both single-camera rigs and multiple-camera rigs can be accommodated. The use of multiple synchronized cameras results in more rapid convergence of the iterative approach. We describe how the algorithm operates, and give examples of its application to three robotic domains: 1) autonomous mobility of ground-based outdoor robots, 2) reconnaissance tasks on ground-based vehicles, and 3) underwater robotics.

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IROS Conference 1996 Conference Paper

A confidence set approach to mobile robot localization

Robert Mandelbaum
Max Mintz

In this paper we pioneer a method which, given an input of mobile robot pose measurements by a sensor-based localization algorithm, produces a minimax risk fixed-size confidence set estimate for the pose of the agent. This work constitutes the first application to the mobile robotics domain of optimal fixed-size confidence-interval decision theory. The approach is evaluated in terms of theoretical capture probability and empirical capture frequency during actual experiments with the mobile agent. The method is compared to several other procedures including the Kalman filter and the maximum likelihood estimator. The minimax approach is found to dominate all the other methods in performance. In particular, for the minimax approach, a very close agreement is achieved between theoretical capture probability and empirical capture frequency. This allows performance to be accurately predicted, greatly facilitating the design of mobile robotic systems, and delineating the tasks that may be performed with a given system.

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IROS Conference 1995 Conference Paper

Cooperative material handling by human and robotic agents: module development and system synthesis

Julie A. Adams
Ruzena Bajcsy
Jana Kosecka
Vijay Kumar 0001
Robert Mandelbaum
Max Mintz
Richard P. Paul
Curtis Wang

Presents a collaborative effort to design and implement a cooperative material handling system by a small team of human and robotic agents in an unstructured indoor environment. The authors' approach makes fundamental use of the human agents' expertise for aspects of task planning, task monitoring, and error recovery. The authors' system is neither fully autonomous nor fully teleoperated. It is designed to make effective use of the human's abilities within the present state of the art of autonomous systems. The authors' robotic agents refer to systems which are each equipped with at least one sensing modality and which possess some capability for self-orientation and/or mobility. The authors' robotic agents are not required to be homogeneous with respect to either capabilities or function. The authors' research stresses both paradigms and testbed experimentation. Theory issues include the requisite coordination principles and techniques which are fundamental to a cooperative multiagent system's basic functioning. The authors have constructed an experimental distributed multiagent-architecture testbed facility. The required modular components of this testbed are currently operational and have been tested individually. The authors' current research focuses on the agents' integration in a scenario for cooperative material handling.

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IROS Conference 1995 Conference Paper

Feature-based localization using fixed ultrasonic transducers

Robert Mandelbaum
Max Mintz

Describes an approach for mobile robot localization based on geometric features extracted from ultrasonic data. In previous work the authors (1994) proposed a multi-stage approach to sonar data clustering which extracts planar surface features in real time using transducers fixed relative to the mobile platform. The method is efficient, precise, and robust in the face of both measurement noise and significant dead-reckoning error. In this work the authors apply the feature extraction algorithm to the problem of localization in a partially known environment. The authors describe an approach for establishing correspondences between extracted and map features. A least-squares approach to mobile robot pose estimation is delineated which is linear in the number of extracted features. The decoupling of the correspondence-matching and pose-estimation stages offers advantages in speed and precision. There are no constraints on the trajectory to be followed for localization except that sufficiently large portions of features be ensonified to allow clustering. Preliminary experiments indicate the utility of the approach, especially for accurate orientation estimation.

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