VB-T PHD-SLAM: Efficient SLAM under heavy-tailed noise

Abstract

Abstract To address the challenge of simultaneous localization and mapping (SLAM) in the presence of heavy-tailed noise, this papper introduces a robust probability hypothesis density (PHD) SLAM algorithm. This algorithm models measurement noise using the Student's t-distribution, which better captures the heavy-tailed nature of the noise. Since the prior density is assumed to be Gaussian mixture form, the posterior density is no longer Gaussian mixture form after the likelihood update of the t-distribution. A variational Bayesian approach is employed to ensure computable multi-target densities during filtering, minimizing the Kullback-Leibler divergence to obtain an approximate solution for the new marginal likelihood function. Then a new closed-form recursion of PHD-SLAM is derived by using t-distribution. Simulation results and real-world validations demonstrate that the proposed algorithm outperforms PHD-SLAM 1.0 and PHD-SLAM 2.0 in terms of both localization and mapping accuracy while maintaining computational efficiency in SLAM scenarios affected by heavy-tailed noise.