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In this paper, simultaneous localization and mapping (SLAM) has been developed; it solves the mapping problem of unknown environments while simultaneously localizing the vehicle using the constructed map. In contrast to the conventional navigation systems, such as GNSS and terrain and image matching systems, no infrastructure or a priori information about the environment is required, thus making it quite attractive for standalone or aided navigation in global positioning system (GPS)-denied environments. Based on the seminal work by 13 and 14, there have been two types of approaches: filtering and graph optimization.Because this article aims for a real-time solution, only the filtering-based approach is summarized here. Filtering-based SLAM solutions are mostly based on the extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filter known as FastSLAM, and sparse extended information filter (SEIF).
Kim et al. (Tue,) studied this question.