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)
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 …