2 code implementations • 12 Jul 2022 • David M. Rosen
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.
1 code implementation • 2 Oct 2021 • Qiangqiang Huang, Can Pu, Kasra Khosoussi, David M. Rosen, Dehann Fourie, Jonathan P. How, John J. Leonard
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.
no code implementations • 8 Mar 2021 • David M. Rosen, Kevin J. Doherty, Antonio Teran Espinoza, John J. Leonard
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.
1 code implementation • 6 Aug 2020 • Frank Dellaert, David M. Rosen, Jing Wu, Robert Mahony, Luca Carlone
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.
1 code implementation • 1 Jun 2020 • Valentin Peretroukhin, Matthew Giamou, David M. Rosen, W. Nicholas Greene, Nicholas Roy, Jonathan Kelly
Accurate rotation estimation is at the heart of robot perception tasks such as visual odometry and object pose estimation.