Review of Kalman filter variants for SLAM in mobile robotics with linearization and covariance initialization - Muhammad Haniff, Gusrial; Nur Aqilah, Othman; Hamzah, Ahmad; Mohd Hasnun Ariff, Hassan (National Research and Innovation Agency (BRIN), 2025)
Simultaneous Localization and Mapping (SLAM) has become a foundational concept in robotics navigation in which enabling autonomous systems to build maps of unknown environments while estimating their own position. This article aims to provide a comprehensive review of SLAM concept in the context of mobile robotics navigation by focusing on theoretical principles, estimation problem, algorithm involved and related application. The existing literature is systematically analyzed and classified based on three main perspectives of navigation which are localization, mapping and path planning. Particular attention is given to Kalman filters and its variants in SLAM-based systems along with crucial consideration of the linearization and covariance initialization. This article identifies strengths and limitations of current SLAM approaches. Therefore, this article concludes by outlining research gaps and recommending directions for future exploration, with the intention of serving as a foundation for continued innovation in SLAM-based robotic navigation systems.