On the Tunable Sparse Graph Solver for Pose Graph Optimization in Visual SLAM Problems

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.

Authors

• Chieh Chou
• Di Wang 0020
• Dezhen Song
• Timothy A. Davis 0001

Keywords

• Visualization
• Gradient methods
• Simultaneous localization and mapping
• Accuracy
• Linear programming
• Mobile handsets
• Optimization
• Intelligent robots
• Visual Problems
• Graph Optimization
• Pose Graph Optimization
• GraphSolver
• Optimization Problem
• Open Data
• Power Constraint
• Cholesky Decomposition
• Optimization Solver
• Computational Constraints
• Covariance Matrix
• Mobile Devices
• Computer Vision
• Cost Function
• Jacobian Matrix
• Accuracy Loss
• Vertices
• Physical Experiments
• Lower Triangular
• Camera Pose
• Bundle Adjustment
• Landmark Localization
• Times Speedup
• Schur Complement
• Preset Threshold
• Direct Solver
• Normal Equations
• Random Sample Consensus