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Improving obstacle boundary representations in predictive occupancy mapping

https://doi.org/10.1016/j.robot.2022.104077

Pearson, Erik; Doherty, Kevin; Englot, Brendan (July 2022, Robotics and Autonomous Systems)

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Variational Filtering with Copula Models for SLAM

https://doi.org/10.1109/IROS45743.2020.9341404

Martin, John D.; Doherty, Kevin; Cyr, Caralyn; Englot, Brendan; Leonard, John (October 2020, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems)
null (Ed.)
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Learning-Aided 3-D Occupancy Mapping With Bayesian Generalized Kernel Inference

https://doi.org/10.1109/TRO.2019.2912487

Doherty, Kevin; Shan, Tixiao; Wang, Jinkun; Englot, Brendan (August 2019, IEEE Transactions on Robotics)

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Approximate Distributed Spatiotemporal Topic Models for Multi-Robot Terrain Characterization

https://doi.org/10.1109/IROS.2018.8594442

Doherty, Kevin; Flaspohler, Genevieve; Roy, Nicholas; Girdhar, Yogesh (October 2018, IEEE)

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Bayesian Generalized Kernel Inference for Terrain Traversability Mapping

Shan, Tixiao; Doherty, Kevin; Wang, Jinkun; Englot, Brendan (October 2018, Proceedings of the 2nd Conference on Robot Learning)

We propose a new approach for traversability mapping with sparse lidar scans collected by ground vehicles, which leverages probabilistic inference to build descriptive terrain maps. Enabled by recent developments in sparse kernels, Bayesian generalized kernel inference is applied sequentially to the related problems of terrain elevation and traversability inference. The first inference step allows sparse data to support descriptive terrain modeling, and the second inference step relieves the burden typically associated with traversability computation. We explore the capabilities of the approach over a variety of data and terrain, demonstrating its suitability for online use in real-world applications.
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