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Improving Robot Navigation Through Self-Supervised Online Learning
B. Sofman, E.L. Ratliff, J. Bagnell, J. Cole, N. Vandapel, and A. Stentz
Journal of Field Robotics, Vol. 23, No. 12, December, 2006.

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In mobile robotics, there are often features that, while potentially powerful for improving navigation, prove difficult to profit from as they generalize poorly to novel situations. Overhead imagery data, for instance, has the potential to greatly enhance autonomous robot navigation in complex outdoor environments. In practice, reliable and effective automated interpretation of imagery from diverse terrain, environmental conditions, and sensor varieties proves challenging. Similarly, fixed techniques that successfully interpret on-board sensor data across many environments begin to fail past short ranges as the density and accuracy necessary for such computation quickly degrade and features that are able to be computed from distant data are very domain-specific. We introduce an online, probabilistic model to effectively learn to use these scope-limited features by leveraging other features that, while perhaps otherwise more limited, generalize reliably. We apply our approach to provide an efficient, self-supervised learning method that accurately predicts traversal costs over large areas from overhead data. We present results from field testing on-board a robot operating over large distances in various off-road environments. Additionally, we show how our algorithm can be used offline with overhead data to produce a priori traversal cost maps and detect misalignments between overhead data and estimated vehicle positions. This approach can significantly improve the versatility of many unmanned ground vehicles by allowing them to traverse highly varied terrains with increased performance.

Associated center: NREC
Associated project: UGCV PerceptOR Integrated

Number of pages: 24

B. Sofman, E.L. Ratliff, J. Bagnell, J. Cole, N. Vandapel, and A. Stentz, "Improving Robot Navigation Through Self-Supervised Online Learning," Journal of Field Robotics, Vol. 23, No. 12, December, 2006.

@article{Sofman_2006_5603,
   author = "Boris Sofman and Ellie Lin Ratliff and James (Drew) Bagnell and John Cole and Nicolas Vandapel and Anthony (Tony) Stentz",
   title = "Improving Robot Navigation Through Self-Supervised Online Learning",
   journal = "Journal of Field Robotics",
   month = "December",
   year = "2006",
   volume = "23",
   number = "12"
}