Enhanced Data Association Process in SLAM Algorithm

This paper presents a practical improvement for the data association process in SLAM algorithms by combining Euclidean distance with Mahalanobis distance. The algorithm efficiently narrows down the search scope for feature matching by first applying simpler Euclidean distance calculations before computing more complex Mahalanobis distances. Simulation results using synthetic data demonstrate that our enhanced method significantly reduces computational load and improves association efficiency without increasing incorrect associations.