The DARPA PerceptOR evaluation experiments

Krotkov, Eric; Fish, Scott; Jackel, L. D.; Mcbride, Bill; Perschbacher, Mike; Pippine, Jim

doi:10.1007/s10514-006-9000-0

Cited by 46 publications

(32 citation statements)

References 13 publications

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“…A Demo III followon activity, PerceptOR, and LAGR also involved systematic, quantitative field testing. For results of DemoIII, RCTA, and PerceptOR, see (Shoemaker and Bornstein, 2000;Technology Development for Army Unmanned Ground Vehicles, 2002;Bornstein and Shoemaker, 2003;Bodt and Camden, 2004;Krotkov et al, 2007) and references therein. LAGR focused on applying learning methods to autonomous navigation.…”

Section: Historical Perspectivementioning

confidence: 99%

Computer Vision on Mars

et al. 2007

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Increasing the level of spacecraft autonomy is essential for broadening the reach of solar system exploration. Computer vision has and will continue to play an important role in increasing autonomy of both spacecraft and Earthbased robotic vehicles. This article addresses progress on computer vision for planetary rovers and landers and has four main parts. First, we review major milestones in the development of computer vision for robotic vehicles over the last four decades. Since research on applications for Earth and space has often been closely intertwined, the review includes elements of both. Second, we summarize the design and performance of computer vision algorithms used on Mars in the NASA/JPL Mars Exploration Rover (MER) mission, which was a major step forward in the use of computer vision in space. These algorithms did stereo vision and visual odometry for rover navigation and feature tracking for horizontal velocity estimation for the landers. Third, we summarize ongoing research to improve vision systems for planetary rovers, which includes various aspects of noise reduction, FPGA implementation, and vision-based slip perception. Finally, we briefly survey other opportunities for computer vision to impact rovers, landers, and orbiters in future solar system exploration missions.

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Section: Historical Perspectivementioning

confidence: 99%

Computer Vision on Mars

et al. 2007

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“…Navigation behaviors have been dominated over the last decade by interest in learning [13,14] and, more specifically, applications of Bayesian map-building [15]. Even in their more relaxed topological representations [16], such methods are committed to repeated measurements as a necessary means of discovery, even when used on legged platforms [11].…”

Section: B Contributionsmentioning

confidence: 99%

Autonomous legged hill and stairwell ascent

Johnson

Hale

Haynes

et al. 2011

2011 IEEE International Symposium on Safety, Security, and Rescue Robotics

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This paper documents near-autonomous negotiation of synthetic and natural climbing terrain by a rugged legged robot, achieved through sequential composition of appropriate perceptually triggered locomotion primitives. The first, simple composition achieves autonomous uphill climbs in unstructured outdoor terrain while avoiding surrounding obstacles such as trees and bushes. The second, slightly more complex composition achieves autonomous stairwell climbing in a variety of different buildings. In both cases, the intrinsic motor competence of the legged platform requires only small amounts of sensory information to yield near-complete autonomy. Both of these behaviors were developed using X-RHex, a new revision of RHex that is a laboratory on legs, allowing a style of rapid development of sensorimotor tasks with a convenience near to that of conducting experiments on a lab bench. Applications of this work include urban search and rescue as well as reconnaissance operations in which robust yet simple-to-implement autonomy allows a robot access to difficult environments with little burden to a human operator. Abstract -This paper documents near-autonomous negotiation of synthetic and natural climbing terrain by a rugged legged robot, achieved through sequential composition of appropriate perceptually triggered locomotion primitives. The first, simple composition achieves autonomous uphill climbs in unstructured outdoor terrain while avoiding surrounding obstacles such as trees and bushes. The second, slightly more complex composition achieves autonomous stairwell climbing in a variety of different buildings. In both cases, the intrinsic motor competence of the legged platform requires only small amounts of sensory information to yield near-complete autonomy. Both of these behaviors were developed using X-RHex, a new revision of RHex that is a laboratory on legs, allowing a style of rapid development of sensorimotor tasks with a convenience near to that of conducting experiments on a lab bench. Applications of this work include urban search and rescue as well as reconnaissance operations in which robust yet simple-to-implement autonomy allows a robot access to difficult environments with little burden to a human operator.

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“…Research in autonomous robot navigation has many decades of history (Bellutta, Matthies, Owens, & Rankin, 2000;Chang, Qui, & Nitao, 1986;Kanade, Thorpe, & Whittaker, 1986;Kelly & Stentz, 1997;Kelly et al, 2006;Krotkov et al, 2007;Matthies et al, 1998;Seo & Urmson, 2008;Singh & Digney, 1999). The domain is generally divided into indoor and outdoor categories, with outdoor navigation in turn generally treated as either structured (e.g., road following where landmarks or other man-made structure is available for navigation) or unstructured (areas that have no regular properties that can be used for navigation).…”

Section: Related Workmentioning

confidence: 99%

Learning terrain segmentation with classifier ensembles for autonomous robot navigation in unstructured environments

Procopio

Mulligan

Grudić

2009

Journal of Field Robotics

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Autonomous robot navigation in unstructured outdoor environments is a challenging area of active research and is currently unsolved. The navigation task requires identifying safe, traversable paths that allow the robot to progress toward a goal while avoiding obstacles. Stereo is an effective tool in the near field, but used alone leads to a common failure mode in autonomous navigation in which suboptimal trajectories are followed due to nearsightedness, or the robot's inability to distinguish obstacles and safe terrain in the far field. This can be addressed through the use of machine learning methods to accomplish near-to-far learning, in which near-field terrain appearance and stereo readings are used to train models able to predict far-field terrain. This paper proposes to enhance existing, memoryless near-to-far learning approaches through the use of classifier ensembles that allow terrain models trained on data seen at different points in time to be preserved and referenced later. These stored models serve as memory, and we show that they can be leveraged for more effective far-field terrain classification on future images seen by the robot. A five-factor, full-factorial, repeated-measures experimental evaluation is performed on hand-labeled data sets taken directly from the problem domain. The experiments result in many statistically significant findings, the most important being that the proposed near-to-far Best-K Ensemble Algorithm, with appropriate parameter selection, outperforms the single-model, nonensemble baseline approach in far-field terrain classification. Several other findings that inform the use of near-to-far ensemble methods are also presented.

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The DARPA PerceptOR evaluation experiments

Cited by 46 publications

References 13 publications

Computer Vision on Mars

Computer Vision on Mars

Autonomous legged hill and stairwell ascent

Learning terrain segmentation with classifier ensembles for autonomous robot navigation in unstructured environments

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