A Certifiable Algorithm for Simultaneous Shape Estimation and Object Tracking
Lorenzo Shaikewitz, Samuel Ubellacker, Luca Carlone

TL;DR
This paper introduces a certifiably optimal algorithm for simultaneous shape estimation and object pose tracking using 3D keypoints, applicable to dynamic environments like manipulation and autonomous vehicles.
Contribution
It presents the first certifiably optimal approach for category-level shape and pose tracking, solving a non-convex fixed-lag smoothing problem with a small semidefinite relaxation.
Findings
Achieves certifiable optimality despite non-convexity.
Effective outlier rejection with shape and time tests.
Demonstrates strong performance on synthetic and real data.
Abstract
Applications from manipulation to autonomous vehicles rely on robust and general object tracking to safely perform tasks in dynamic environments. We propose the first certifiably optimal category-level approach for simultaneous shape estimation and pose tracking of an object of known category (e.g. a car). Our approach uses 3D semantic keypoint measurements extracted from an RGB-D image sequence, and phrases the estimation as a fixed-lag smoothing problem. Temporal constraints enforce the object's rigidity (fixed shape) and smooth motion according to a constant-twist motion model. The solutions to this problem are the estimates of the object's state (poses, velocities) and shape (paramaterized according to the active shape model) over the smoothing horizon. Our key contribution is to show that despite the non-convexity of the fixed-lag smoothing problem, we can solve it to certifiable…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Face and Expression Recognition · Image Retrieval and Classification Techniques
