Journal of Field Robotics

Cover image for Vol. 31 Issue 6

Edited By: Sanjiv Singh

Impact Factor: 1.88

ISI Journal Citation Reports © Ranking: 2013: 5/21 (Robotics)

Online ISSN: 1556-4967

Most Cited

Read the most cited articles published since 2009

Generation and Visualization of Large-Scale Three-Dimensional Reconstructions from Underwater Robotic Surveys
Matthew Johnson-Roberson, Oscar Pizarro, Stefan B. Williams, Ian Mahon

Robust, scalable simultaneous localization and mapping (SLAM) algorithms support the successful deployment of robots in real-world applications. In many cases these platforms deliver vast amounts of sensor data from large-scale, unstructured environments. These data may be difficult to interpret by end users without further processing and suitable visualization tools. We present a robust, automated system for large-scale three-dimensional (3D) reconstruction and visualization that takes stereo imagery from an autonomous underwater vehicle (AUV) and SLAM-based vehicle poses to deliver detailed 3D models of the seafloor in the form of textured polygonal meshes. Read the entire abstract.

Volume 27, Issue 1, January/February 2010, pages 21-51

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1-Point RANSAC for Extended Kalman Filtering: Application to Real-Time Structure from Motion and Visual Odometry
Javier Civera, Oscar G. Grasa1, Andrew J. Davison, J. M. M. Montiel

Random sample consensus (RANSAC) has become one of the most successful techniques for robust estimation from a data set that may contain outliers. It works by constructing model hypotheses from random minimal data subsets and evaluating their validity from the support of the whole data. In this paper we present a novel combination of RANSAC plus extended Kalman filter (EKF) that uses the available prior probabilistic information from the EKF in the RANSAC model hypothesize stage. Read the entire abstract.

Volume 27, Issue 5, September/October 2010, pages 609-631

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Coordinated Control of an Underwater Glider Fleet in an Adaptive Ocean Sampling Field Experiment in Monterey Bay
Naomi E. Leonard, Derek A. Paley, Russ E. Davis, David M. Fratantoni, Francois Lekien, Fumin Zhang

A full-scale adaptive ocean sampling network was deployed throughout the month-long 2006 Adaptive Sampling and Prediction (ASAP) field experiment in Monterey Bay, California. One of the central goals of the field experiment was to test and demonstrate newly developed techniques for coordinated motion control of autonomous vehicles carrying environmental sensors to efficiently sample the ocean. We describe the field results for the heterogeneous fleet of autonomous underwater gliders that collected data continuously throughout the month-long experiment. Read the entire abstract.

Volume 27, Issue 6, November/December 2010, pages 718-740

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Three-Dimensional Mapping with Time-of-Flight Cameras
Stefan May, David Droeschel, Dirk Holz, Stefan Fuchs, Ezio Malis, Andreas Nüchter, Joachim Hertzberg

This article investigates the use of time-of-flight (ToF) cameras in mapping tasks for autonomous mobile robots, in particular in simultaneous localization and mapping (SLAM) tasks. Although ToF cameras are in principle an attractive type of sensor for three-dimensional (3D) mapping owing to their high rate of frames of 3D data, two features make them difficult as mapping sensors, namely, their restricted field of view and influences on the quality of range measurements by high dynamics in object reflectivity; in addition, currently available models suffer from poor data quality in a number of aspects. Read the entire abstract.

Volume 26, Issue 11-12, November-December 2009, pages 934-965

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Magnebike: A Magnetic Wheeled Robot with High Mobility for Inspecting Complex-Shaped Structures
Fabien Tâche, Wolfgang Fischer, Gilles Caprari, Roland Siegwart, Roland Moser, Francesco Mondada

This paper describes the Magnebike robot, a compact robot with two magnetic wheels in a motorbike arrangement, which is intended for inspecting the inner casing of ferromagnetic pipes with complex-shaped structures. The locomotion concept is based on an adapted magnetic wheel unit integrating two lateral lever arms. These arms allow for slight lifting off the wheel in order to locally decrease the magnetic attraction force when passing concave edges, as well as laterally stabilizing the wheel unit. The robot has the main advantage of being compact (180 x 130 x 220 mm) and mechanically simple: it features only five active degrees of freedom (two driven wheels each equipped with an active lifter stabilizer and one steering unit). Read the entire abstract.

Volume 26, Issue 5, May 2009, pages 453-476

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Online Three-Dimensional SLAM by Registration of Large Planar Surface Segments and Closed-Form Pose-Graph Relaxation
Kaustubh Pathak, Andreas Birk, Narunas Vaskevicius, Max Pfingsthorn, Sören Schwertfeger, Jann Poppinga

A fast pose-graph relaxation technique is presented for enhancing the consistency of three-dimensional (3D) maps created by registering large planar surface patches. The surface patches are extracted from point clouds sampled from a 3D range sensor. The plane-based registration method offers an alternative to the state-of-the-art algorithms and provides advantages in terms of robustness, speed, and storage. One of its features is that it results in an accurate determination of rotation, although a lack of predominant surfaces in certain directions may result in translational uncertainty in those directions. Hence, a loop-closing and relaxation problem is formulated that gains significant speed by relaxing only the translational errors and utilizes the full-translation covariance determined during pairwise registration. Read the entire abstract.

Volume 27, Issue 1, January/February 2010, pages 52-84

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Mapping, navigation, and learning for off-road traversal
Kurt Konolige, Motilal Agrawal, Morten Rufus Blas, Robert C. Bolles, Brian Gerkey, Joan Solà, Aravind Sundaresan

The challenge in the DARPA Learning Applied to Ground Robots (LAGR) project is to autonomously navigate a small robot using stereo vision as the main sensor. During this project, we demonstrated a complete autonomous system for off-road navigation in unstructured environments, using stereo vision as the main sensor. The system is very robust—we can typically give it a goal position several hundred meters away and expect it to get there. In this paper we describe the main components that comprise the system, including stereo processing, obstacle and free space interpretation, long-range perception, online terrain traversability learning, visual odometry, map registration, planning, and control. At the end of 3 years, the system we developed outperformed all nine other teams in final blind tests over previously unseen terrain. Read the entire abstract.

Volume 26, Issue 1, January 2009, pages 88-113

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Learning long-range vision for autonomous off-road driving
Raia Hadsell, Pierre Sermanet, Jan Ben, Ayse Erkan, Marco Scoffier, Koray Kavukcuoglu, Urs Muller, Yann LeCun

Most vision-based approaches to mobile robotics suffer from the limitations imposed by stereo obstacle detection, which is short range and prone to failure. We present a self-supervised learning process for long-range vision that is able to accurately classify complex terrain at distances up to the horizon, thus allowing superior strategic planning. The success of the learning process is due to the self-supervised training data that are generated on every frame: robust, visually consistent labels from a stereo module; normalized wide-context input windows; and a discriminative and concise feature representation. Read the entire abstract.

Volume 26, Issue 2, February 2009, pages 120-144

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Differentially constrained mobile robot motion planning in state lattices
Mihail Pivtoraiko, Ross A. Knepper, Alonzo Kelly

We present an approach to the problem of differentially constrained mobile robot motion planning in arbitrary cost fields. The approach is based on deterministic search in a specially discretized state space. We compute a set of elementary motions that connects each discrete state value to a set of its reachable neighbors via feasible motions. Thus, this set of motions induces a connected search graph. The motions are carefully designed to terminate at discrete states, whose dimensions include relevant state variables (e.g., position, heading, curvature, and velocity). The discrete states, and thus the motions, repeat at regular intervals, forming a lattice. We ensure that all paths in the graph encode feasible motions via the imposition of continuity constraints on state variables at graph vertices and compliance of the graph edges with a differential equation comprising the vehicle model. Read the entire abstract.

Volume 26, Issue 3, March 2009, pages 308-333

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Vision-based navigation through urban canyons
Stefan Hrabar, Gaurav Sukhatme

We address the problem of navigating unmanned vehicles safely through urban canyons in two dimensions using only vision-based techniques. Two commonly used vision-based obstacle avoidance techniques (namely stereo vision and optic flow) are implemented on an aerial and a ground-based robotic platform and evaluated for urban canyon navigation. Optic flow is evaluated for its ability to produce a centering response between obstacles, and stereo vision is evaluated for detecting obstacles to the front. We also evaluate a combination of these two techniques, which allows a vehicle to detect obstacles to the front while remaining centered between obstacles to the side. Through experiments on an unmanned ground vehicle and in simulation, this combination is shown to be beneficial for navigating urban canyons, including T-junctions and 90-deg bends. Read the entire abstract.

Volume 26, Issue 5, May 2009, pages 431-452

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