Person:
Dellaert, Frank

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https://hdl.handle.net/1853/71202
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School of Interactive Computing
School established in 2007
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Monocular Parallel Tracking and Mapping with Odometry Fusion for MAV Navigation in Feature-Lacking Environments

2013-11 , Ta, Duy-Nguyen , Ok, Kyel , Dellaert, Frank

Despite recent progress, autonomous navigation on Micro Aerial Vehicles with a single frontal camera is still a challenging problem, especially in feature-lacking environ- ments. On a mobile robot with a frontal camera, monoSLAM can fail when there are not enough visual features in the scene, or when the robot, with rotationally dominant motions, yaws away from a known map toward unknown regions. To overcome such limitations and increase responsiveness, we present a novel parallel tracking and mapping framework that is suitable for robot navigation by fusing visual data with odometry measurements in a principled manner. Our framework can cope with a lack of visual features in the scene, and maintain robustness during pure camera rotations. We demonstrate our results on a dataset captured from the frontal camera of a quad- rotor flying in a typical feature-lacking indoor environment.

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4D View Synthesis: Navigating Through Time and Space

2007 , Sun, Mingxuan , Schindler, Grant , Kang, Sing Bing , Dellaert, Frank

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Rao-Blackwellized Importance Sampling of Camera Parameters from Simple User Input with Visibility Preprocessing in Line Space

2006-06 , Quennesson, Kevin , Dellaert, Frank

Users know what they see before where they are: it is more natural to talk about high level visibility information ("I see such object") than about one's location or orientation. In this paper we introduce a method to find in 3D worlds a density of viewpoints of camera locations from high level visibility constraints on objects in this world. Our method is based on Rao-Blackwellized importance sampling. For efficiency purposes, the proposal distribution used for sampling is extracted from a visibility preprocessing technique adapted from computer graphics. We apply the method for finding in a 3D city model of Atlanta the virtual locations of real-world cameras and viewpoints.

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