This work presents a novel method, called Controlled Filter with Active Alignment (CFAA), to address the problem of Simultaneous Localization and Mapping (SLAM). SLAM aims to map an unknown environment while estimating the trajectory of a mobile agent moving within that environment. CFAA combines the two fundamental pillars of SLAM, scan alignment, and loop closure, into a single process. The method is inspired by human perception of self-location and utilizes a mental map for guidance. CFAA employs a Gaussian distribution to estimate possible poses and conducts the alignment process in cycles, where each cycle is influenced by the results of the previous cycle. The Active Alignment mechanism is used in each cycle to determine the quality of each of the possible poses, enabling more accurate simultaneous localization and mapping. Benchmarking tests were conducted on five public datasets, confirming the effectiveness and efficiency of the proposed method.