Robust Landmark Estimation and Unscented Particle Sampling for SLAM in Dynamic Outdoor Environment
Atsushi Sakai, Teppei Saitoh, and Yoji Kuroda
Department of Mechanical Engineering, Meiji University, 1-1-1 Higashimita, Tama-ku, Kawasaki, Kanagawa 214-8571, Japan
In this paper, we propose a set of techniques for accurate and practical Simultaneous Localization And Mapping (SLAM) in dynamic outdoor environments. The techniques are categorized into Landmark estimation and Unscented particle sampling. Landmark estimation features stable feature detection and data management for estimating landmarks accurately, robustly, and at a low-calculation cost. The stable feature detection removes dynamic objects and sensor noise with scan subtraction, detects feature points sparsely and evenly, and sets data association parameters with landmark density. The data management calculates landmark existence probability and spurious landmarks are removed, utilizes landmark exclusivity for data association, and predicts importance weights using the observation range. Unscented particle sampling is based on Unscented Transformation for accurate SLAM. Simulation results of SLAM using our landmark estimation and experimental results of our SLAM in dynamic outdoor environments are presented and discussed. The results show that our landmark estimation decrease SLAM calculation time and maximum position error by 80% compared to conventional landmark estimation, and position estimation of SLAM with Unscented particle sampling ismore accurate than FastSLAM2.0 in dynamic outdoor environments.
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