Autonomous robotics plays a pivotal role to simplify humanmachine interaction while meeting the current industrial demands. In that process, machine intelligence plays a dominant role during the decision making in the operational state-space. Primarily, this decision making and control mechanism relies on sensing and actuation. Simultaneous localization and mapping (SLAM) is the most advanced technique that facilitates both sensing and actuation to achieve autonomy for robots. This work aims to collate multi-dimensional aspects of simultaneous localization and mapping techniques primarily in the purview of both deterministic and probabilistic frameworks. This investigation on SLAM classification is further elaborated into different categories such as Feature-based SLAM and Optimization based SLAM. In this work, the chronological evolution of SLAM technique develops a comprehensive understanding among the concerned research community.

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