Enhanced Nearest Neighbor Method for SLAM Data Association

This paper introduces an enhancement to the most commonly used nearest neighbor method in SLAM data association. Traditional approaches relying on Mahalanobis distance calculations require computing distances between all features and each measurement, which involves extensive matrix multiplication operations. Our proposed method utilizes Euclidean distance calculations between feature estimated positions and vehicle predicted positions, effectively avoiding these matrix multiplications and thereby reducing the algorithm's computational complexity. Implementation-wise, this approach simplifies the distance calculation process by using straightforward Euclidean distance formulas rather than complex covariance matrix operations. From an algorithmic perspective, the method maintains the same association performance as global nearest neighbor methods while being significantly more efficient. Consequently, the improved method not only enhances real-time performance but also preserves association accuracy.