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Found 5 papers, 4 papers with code
Accelerating Certifiable Estimation with Preconditioned Eigensolvers
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 settings.
Incremental Non-Gaussian Inference for SLAM Using Normalizing Flows
This paper presents normalizing flows for incremental smoothing and mapping (NF-iSAM), a novel algorithm for inferring the full posterior distribution in SLAM problems with nonlinear measurement models and non-Gaussian factors.
Advances in Inference and Representation for Simultaneous Localization and Mapping
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, and control.
Shonan Rotation Averaging: Global Optimality by Surfing $SO(p)^n$
Shonan Rotation Averaging is a fast, simple, and elegant rotation averaging algorithm that is guaranteed to recover globally optimal solutions under mild assumptions on the measurement noise.
A Smooth Representation of Belief over SO(3) for Deep Rotation Learning with Uncertainty
Accurate rotation estimation is at the heart of robot perception tasks such as visual odometry and object pose estimation.
