A Decoupled and Linear Framework for Global Outlier Rejection over Planar Pose Graph
- Resource Type
- Conference
- Authors
- Wu, Tianyue; Gao, Fei
- Source
- 2023 IEEE International Conference on Robotics and Automation (ICRA) Robotics and Automation (ICRA), 2023 IEEE International Conference on. :4150-4156 May, 2023
- Subject
- Robotics and Control Systems
Transmission line matrix methods
Automation
Linearity
Estimation
Benchmark testing
Odometry
Kernel
- Language
We propose a robust framework for planar pose graph optimization contaminated by loop closure outliers. Our framework rejects outliers by first decoupling the robust PGO problem wrapped by a Truncated Least Squares kernel into two subproblems. Then, the framework introduces a linear angle representation to rewrite the first subproblem that is originally formulated in rotation matrices. The framework is configured with the Graduated Non-Convexity (GNC) algorithm to solve the two non-convex subproblems in succession without initial guesses. Thanks to the linearity property of the angle representation, our framework requires only a linear solver to optimally solve the optimization problems encountered in GNC. We extensively validate the proposed framework, named DEGNC- LAF (DEcoupled Graduated Non-Convexity with Linear Angle Formulation) in planar PGO benchmarks. It turns out that it runs significantly (sometimes up to over 30 times) faster than the standard and general-purpose GNC while resulting in high-quality estimates.