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Title: Multi-sensor Mapping in Natural Environment: Three-Dimensional Reconstruction and Temporal Alignment
Committee:
Dr. Pradalier, Advisor
Dr. Owen, Co-Advisor
Dr. Yezzi, Chair
Dr. Mazumdar
Abstract: The objective of the proposed research is the adaptation and development of robotic techniques, suitable for geometric three dimensional reconstruction of natural environments, in the context of a wearable sensor suite. This has been partly achieved using sensor fusion techniques, that combine data generated by cameras, lasers and an inertial measurement unit, in order to geometrically reconstruct the surrounding scene as well as estimate the trajectory. By supporting cameras with laser depth information, we show that it is possible to stabilize and recover scale for visual maps. We also show that factor graphs are powerful tools for sensor fusion, and can be used for a generalized approach involving multiple sensors. What's further planned is the definition of a point cloud topology using learning-based techniques to generate semantic representations of natural environment constituents such as trees. Finally, a temporal alignment of the global map is planned by fusion of semantic information with geometric constraints.
