AA 273 Final Project

The SE-Sync Algorithm for Pose-Graph SLAM.


We study the SE-Sync algorithm and how it can be utilized for pose-graph SLAM. We begin by presenting a historical review of the general SLAM problem and the optimization based paradigm of pose-graph SLAM. We then proceed to carefully define the pose-graph SLAM problem and present an intuitive but still sufficiently complete treatment of the SE-Sync algorithm and how it is applied to solve the corresponding optimization problem.

Our main contribution is an implementation of SE-Sync in a ROS compatible SLAM system obtained by replacing the back-end optimizer of an existing open source implementation. Utilizing a realistic simulation environment, we compare its performance with the original implementation in four different mapping environments of varying size and complexity, and find that SE-Sync performs equally well or slightly better in all cases.

We finally conclude that application of the SE-Sync algorithm in practical SLAM systems seems promising, but that further work is necessary to determine whether or not the improvement is of significant practical importance.

Course project in AA 273: State Estimation and Filtering for Aerospace Systems, Stanford University