Abstract-Popular problems in robotics and computer vision like simultaneous localization and mapping (SLAM) or structure from motion (SfM) require to solve a least-squares problem that can be effectively represented by factor graphs. The chance to find the global minimum of such problems depends on both the initial guess and the non-linearity of the sensor models. In this paper we propose an approach to determine an approximation of the original problem that has a larger convergence basin. To this end, we employ a divide-and-conquer approach that exploits the structure of the factor graph. Our approach has been validated on real-world and simulated experiments and is able to succeed in finding the global minimum in situations where other state-of-the-art methods fail.
As shown in their work REF , this offers a larger convergence basin than Levenberg-Marquardt and yields convergence to the true solution in real world and simulated scenes where other state-of-the-art methods fail.
Robust optimization of factor graphs by using condensed measurements
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
Computer Science