Incorporating structure from motion uncertainty into image-based pose estimation

Ben T. Ludington; Andrew P. Brown; Michael J. Sheffler; Clark N. Taylor; Stephen Berardi

doi:10.1117/12.2180276

21 May 2015 Incorporating structure from motion uncertainty into image-based pose estimation

Ben T. Ludington, Andrew P. Brown, Michael J. Sheffler, Clark N. Taylor, Stephen Berardi

Proceedings Volume 9473, Geospatial Informatics, Fusion, and Motion Video Analytics V; 94730C (2015) https://doi.org/10.1117/12.2180276
Event: SPIE Defense + Security, 2015, Baltimore, MD, United States

Abstract

A method for generating and utilizing structure from motion (SfM) uncertainty estimates within image-based pose estimation is presented. The method is applied to a class of problems in which SfM algorithms are utilized to form a geo-registered reference model of a particular ground area using imagery gathered during flight by a small unmanned aircraft. The model is then used to form camera pose estimates in near real-time from imagery gathered later. The resulting pose estimates can be utilized by any of the other onboard systems (e.g. as a replacement for GPS data) or downstream exploitation systems, e.g., image-based object trackers. However, many of the consumers of pose estimates require an assessment of the pose accuracy. The method for generating the accuracy assessment is presented. First, the uncertainty in the reference model is estimated. Bundle Adjustment (BA) is utilized for model generation. While the high-level approach for generating a covariance matrix of the BA parameters is straightforward, typical computing hardware is not able to support the required operations due to the scale of the optimization problem within BA. Therefore, a series of sparse matrix operations is utilized to form an exact covariance matrix for only the parameters that are needed at a particular moment. Once the uncertainty in the model has been determined, it is used to augment Perspective-n-Point pose estimation algorithms to improve the pose accuracy and to estimate the resulting pose uncertainty. The implementation of the described method is presented along with results including results gathered from flight test data.

Citation Download Citation

Ben T. Ludington, Andrew P. Brown, Michael J. Sheffler, Clark N. Taylor, and Stephen Berardi "Incorporating structure from motion uncertainty into image-based pose estimation", Proc. SPIE 9473, Geospatial Informatics, Fusion, and Motion Video Analytics V, 94730C (21 May 2015); https://doi.org/10.1117/12.2180276

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