Convex (specifically semidefinite) relaxation provides a powerful approach to constructing robust machine perception systems, enabling the recovery of certifiably globally optimal solutions of challenging estimation problems in many practical …
The first *distributed* algorithm provably capable of recovering correct (*globally optimal*) solutions of SLAM and rotation averaging. Honorable Mention, IEEE Transactions on Robotics King-Sun Fu Memorial Best Paper Award
A fast algorithm for *certifiably globally optimal* rotation averaging. Implemented in the GTSAM library. ECCV 2020 spotlight talk (top 5%)
Build your own certifiably correct machine perception methods
The first practical algorithm *provably* capable of recovering correct (*globally optimal*) solutions of the SLAM problem. Invited article (IJRR Special Issue)
We investigate numerical and computational aspects of the use of convex relaxation for simultaneous localization and mapping (SLAM). Recent work has shown that convex relaxation provides an effective tool for computing, and certifying the correctness …
Many important geometric estimation problems naturally take the form of *synchronization over the special Euclidean group*: estimate the values of a set of unknown poses given noisy measurements of a subset of their pairwise relative transforms. …
The first practical algorithm *provably* capable of recovering correct (*globally optimal*) solutions of the SLAM problem. Best Paper Award (WAFR 2016)