Sean Augenstein

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A hybrid algorithm for real-time frame-to-frame pose estimation during monocular vision-only SLAM/SFM is presented. The algorithm combines concepts from two existing approaches to pose tracking, Bayesian estimation methods and measurement inversion techniques, to achieve in real-time a feasible, smooth estimate of the relative pose between a robotic(More)
A solution is presented to the problem of estimating recursively the 6-DOF pose and 3-D shape of a target, using insights from the SLAM community. The algorithm in this paper is referred to as ‘SLAM-inspired Pose Estimation and Reconstruction’ (or ‘SPEAR’), and is based on the FastSLAM algorithm. In particular, FastSLAM’s use of Rao-Blackwellized particle(More)
A framework for 3D target reconstruction and relative pose estimation through fusion of vision and sparse-pattern range data (e.g. line-scanning LIDAR) is presented. The algorithm augments previous work in monocular vision-only SLAM/SfM to incorporate range data into the overall solution. The aim of this work is to enable a more dense reconstruction with(More)
I am working on machine learning techniques to intelligently track and match features in a sequence of visual images. Specifically, the feature I am tracking is known as the Scale Invariant Feature Transform (SIFT). My project involves using a camera to capture images of the motion of robotic objects in my lab. I used PCA to find a small subset of the SIFT(More)
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