activity
20202023
most citedAdvances in Inference and Representation for Simultaneous Localization and Mapping

2 citations · 2 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CV2023

Multi-Irreducible Spectral Synchronization for Robust Rotation Averaging

Owen Howell, Haoen Huang, David Rosen

Rotation averaging (RA) is a fundamental problem in robotics and computer vision. In RA, the goal is to estimate a set of unknown orientations , gi…

cs.RO2022

SCORE: A Second-Order Conic Initialization for Range-Aided SLAM

Alan Papalia, Joseph Morales, Kevin J. Doherty +2

We present a novel initialization technique for the range-aided simultaneous localization and mapping (RA-SLAM) problem. In RA-SLAM we consider measurements of point-to-point dista…

cs.RO2022

Spectral Measurement Sparsification for Pose-Graph SLAM

Kevin J. Doherty, David M. Rosen, John J. Leonard

Simultaneous localization and mapping (SLAM) is a critical capability in autonomous navigation, but in order to scale SLAM to the setting of "lifelong" SLAM, particularly under mem…

cs.RO2022

Performance Guarantees for Spectral Initialization in Rotation Averaging and Pose-Graph SLAM

Kevin J. Doherty, David M. Rosen, John J. Leonard

In this work we present the first initialization methods equipped with explicit performance guarantees adapted to the pose-graph simultaneous localization and mapping (SLAM) and ro…

cs.RO20212 cited

Advances in Inference and Representation for Simultaneous Localization and Mapping

David M. Rosen, Kevin J. Doherty, Antonio Teran Espinoza +1

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 mo…

cs.CV2020

Shonan Rotation Averaging: Global Optimality by Surfing

Frank Dellaert, David M. Rosen, Jing Wu +2

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 measure…