Good Feature Selection in BA-SLAM

Abstract: SLAM condition scoring as a means to derive robust camera pose estimation methods for bundle-adjustment based SLAM are explored and shown to improve pose accuracy by 30% while increasing runtime by only 2%. The key idea is to perform inlier selection of tracked feature points so that the local pose optimization has strong conditioning. Combining this geometric conditioning with appearance-based match scoring leads to the stated outcomes.

This work is a spiritual extension of an earlier ICRA paper that sought to improve the robustness and run-time properties of an EKF-SLAM method. It explores the same idea but applied to a popular bundle-adjustment SLAM (BA-SLAM) method, ORB-SLAM. Though implemented for ORB-SLAM, the technique is general and can be applied to most any feature-based, BA-SLAM method employing a local pose optimization step for the fast front-end thread.

More text needed here. Add pictures too.