Can I read Performance Guarantees for Spectral Initialization in Rotation Averaging and Pose-Graph SLAM on EtoBox?

Performance Guarantees for Spectral Initialization in Rotation Averaging and Pose-Graph SLAM by Doherty, Kevin J.; Rosen, David M.; Leonard, John J. is a scholarly article available to read on EtoBox.

What is Performance Guarantees for Spectral Initialization in Rotation Averaging and Pose-Graph SLAM about?

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 rotation averaging (RA) problems. SLAM and rotation averaging are typically formalized as large-scale nonconvex point estimation problems, with many bad local minima that can entrap the smooth optimization methods typically applied to solve them; the performance of standard SLAM and RA algorithms thus crucially depends upon the quality of the estimates used to initialize this local search. While many initialization methods for SLAM and RA have appeared in the literature, these are typically obtained as purely heuristic approximations, making it difficult to determine whether (or under what circumstances) these techniques can be reliably deployed. In contrast, in this work we study the problem of initialization through the lens of spectral relaxation. Specifically, we derive a simple spectral relaxation of SLAM and RA, the form of which enables us to exploit classical linear-algebraic techniques (eigenvector perturbation bounds) to control the distance from our spectral estimate to both the (unknown) ground-

Author

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

Published

2022

Language

EN