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Motivated by an emerging theory of robust low-rank matrix representation, inthis paper, we introduce a novel solution for online rigid-body motionregistration. The goal is to develop algorithmic techniques that enable arobust, real-time motion registration solution suitable for low-cost, portable3-D camera devices. Assuming 3-D image features are tracked via a standardtracker, the algorithm first utilizes Robust PCA to initialize a low-rank shaperepresentation of the rigid body. Robust PCA finds the global optimal solutionof the initialization, while its complexity is comparable to singular valuedecomposition. In the online update stage, we propose a more efficientalgorithm for sparse subspace projection to sequentially project new featureobservations onto the shape subspace. The lightweight update stage guaranteesthe real-time performance of the solution while maintaining good registrationeven when the image sequence is contaminated by noise, gross data corruption,outlying features, and missing data. The state-of-the-art accuracy of thesolution is validated through extensive simulation and a real-world experiment,while the system enjoys one to two orders of magnitude speed-up compared towell-established RANSAC solutions. The new algorithm will be released online toaid peer evaluation.

Sparse Online Low-Rank Projection and Outlier Rejection (SOLO) for 3-D Rigid-Body Motion Registration