We develop the first convex relaxation for the general multi-robot range-aided SLAM (RA-SLAM) problem.
We develop the first convex relaxation for the general multi-robot range-aided SLAM (RA-SLAM) problem.
A computationally-tractable method for approximating the *full* Bayesian posterior distribution in challenging high-dimensional, nonlinear, and non-Gaussian inference problems
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, …
State-of-the-art techniques for simultaneous localization and mapping (SLAM) employ iterative nonlinear optimization methods to compute an estimate for robot poses. While these techniques often work well in practice, they do not provide guarantees on …
A robust online optimization method for real-time machine perception. One of the core optimization algorithms in the GTSAM library
Many online inference problems in robotics and AI are characterized by probability distributions whose factor graph representations are sparse. While there do exist some computationally efficient algorithms (e.g. incremental smoothing and mapping …
Many online inference problems in computer vision and robotics are characterized by probability distributions whose factor graph representations are sparse and whose factors are all Gaussian functions of error residuals. Under these conditions, …