The first initialization algorithms for pose-graph SLAM and rotation averaging with *explicit performance guarantees*.
The first initialization algorithms for pose-graph SLAM and rotation averaging with *explicit performance guarantees*.
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
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)
Recent work in 3D Pose Graph Optimization (PGO) shows how a dual Lagrangian formulation of the problem can be used to verify (and possibly certify) the quality of a given solution. A limitation of current approaches is that they relax the positive …
Modern approaches to simultaneous localization and mapping (SLAM) formulate the inference problem as a high-dimensional but sparse nonconvex M-estimation, and then apply general first- or second-order smooth optimization methods to recover a local …