Enhanced Data Association Process in SLAM Algorithm

This paper proposes a simple yet effective improvement for the data association process in SLAM algorithms. Our method integrates both Euclidean distance and Mahalanobis distance for data association, eliminating the need to compute Mahalanobis distances between all map features and all measurements. The implementation first applies Euclidean distance calculations to narrow down the candidate feature search space, followed by more precise Mahalanobis distance evaluations on the reduced set. This two-stage approach significantly optimizes the computational workflow compared to traditional methods. Through simulation experiments using synthetic data, we further validated the effectiveness of our enhanced data association method. The experimental results demonstrate that our approach substantially reduces system computational requirements while improving data association efficiency. Crucially, the method maintains association accuracy without increasing incorrect matches. We believe this improved technique can play a significant role in practical applications, providing strong support for the advancement and optimization of SLAM algorithms. The code implementation typically involves creating a distance threshold mechanism where Euclidean distance serves as an initial filter, followed by Mahalanobis-based probabilistic matching for the shortlisted features.