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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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Tele-immersion is a technology that augments your space with real-time 3D projections of remote spaces thus facilitating the interaction of people from different places in virtually the same environment. Tele-immersion combines 3D scene recovery from computer vision, and rendering and interaction from computer graphics. We describe the real-time 3D scene acquisition using a new algorithm for trinocular stereo. We extend this method in time by combining motion and stereo in order to increase speed and robustness. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This conference paper is available at ScholarlyCommons: http://repository.upenn.edu/cis_papers/23 REAL TIME TRINOCULAR STEREO FOR TELE-IMMERSION Jane Mulligan and Kostas DaniilidisUniversity of Pennsylvania, GRASP Laboratory 3401 Walnut Street, Philadelphia, PA { janem,kostas} @grip.cis.upenn.edu ABSTRACT Tele-immersion is a technology that augments your space with real-time 3D projections of remote spaces thus facilitating the interaction of people from different places in virtually the same environment. Tele-immersion combines 3D scene recovery from computer vision, and rendering and interaction from computer graphics. We describe the realtime 3D scene acquisition using a new algorithm for trinocular stereo We extend this method in time by combining motion and stereo in order to increase speed and robustness.

Abstract-Autonomous robot navigation in outdoor environments remains a challenging and unsolved problem. A key issue is our ability to identify safe or navigable paths far enough ahead of the robot to allow smooth trajectories at acceptable speeds. Colour or texture-based labeling of safe path regions in image sequences is one way to achieve this far field prediction. A challenge for classifiers identifying path and nonpath regions is to make meaningful comparisons of feature vectors at pixels or over a window. Most simple distance metrics cannot use all the information available and therefore the resulting labeling does not tightly capture the visible path. We introduce a new Polynomial Mahalanobis Distance and demonstrate its ability to capture the properties of an initial positive path sample and produce accurate path segmentation with few outliers. Experiments show the method's effectiveness for path segmentation in natural scenes using both colour and texture feature vectors. The new metric is compared with classifications based on Euclidean and standard Mahalanobis distance and produces superior results.

Tele-immersion is a new medium that enables a user to share a virtual space with remote participants. The user is immersed in a rendered 3D-world that is transmitted from a remote site. To acquire this 3D description we apply biand trinocular stereo techniques. The challenge is to compute dense stereo range data at high frame rates, since participants cannot easily communicate if the processing cycle or network latencies are long. Moreover, new views of the received 3D-world must be as accurate as possible. We address both issues of speed and accuracy and we propose a method for combining motion and stereo in order to increase speed and robustness.

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