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
Simultaneous localization and mapping (SLAM) is the process of constructing a global model of an environment from local observations of it; this is a foundational capability for mobile robots, supporting such core functions as planning, navigation, …
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)