On the Tunable Sparse Graph Solver for Pose Graph Optimization in Visual SLAM Problems
Chieh Chou, Di Wang, Dezhen Song, Timothy A. Davis
Abstract
We report a tunable sparse optimization solver that can trade a slight decrease in accuracy for significant speed improvement in pose graph optimization in visual simultaneous localization and mapping (vSLAM). The solver is designed for devices with significant computation and power constraints such as mobile phones or tablets. Two approaches have been combined in our design. The first is a graph pruning strategy by exploiting objective function structure to reduce the optimization problem size which further sparsifies the optimization problem. The second step is to accelerate each optimization iteration in solving increments for the gradient-based search in Gauss-Newton type optimization solver. We apply a modified Cholesky factorization and reuse the decomposition result from last iteration by using Cholesky update/downdate to accelerate the computation. We have implemented our solver and tested it with open source data. The experimental results show that our solver can be twice as fast as the counterpart while maintaining a loss of less than 5% in accuracy.
BibTeX
@inproceedings{iros2019_onthetunablespar,
title = {On the Tunable Sparse Graph Solver for Pose Graph Optimization in Visual SLAM Problems},
author = {Chieh Chou and Di Wang and Dezhen Song and Timothy A. Davis},
booktitle = {IROS 2019},
year = {2019}
}