HYBRID VISUAL–INERTIAL SLAM ARCHITECTURE FOR CONTINUOUS NAVIGATION IN LOW-VISIBILITY CONDITIONS
DOI:
https://doi.org/10.32689/maup.it.2026.1.7Keywords:
autonomous systems, sensor fusion, localization, inertial sensing, adaptive algorithmsAbstract
The relevance of the study is determined by the need to ensure reliable and continuous navigation of autonomous mobile systems under low-visibility conditions, where conventional visual localization methods lose effectiveness, while inertial sensors are subject to error accumulation. Under such conditions, the integration of heterogeneous sensor data within hybrid architectures capable of adapting to variations in input data quality becomes critically important. The aim of the study is to theoretically substantiate and practically generalize approaches to the development of a hybrid visual–inertial SLAM architecture focused on ensuring continuous and robust navigation in low-visibility environments. The research methods are based on system analysis, generalization of modern scientific approaches, comparative analysis of algorithmic solutions, and structural-functional modeling of visual–inertial data integration processes in navigation systems. As a result of the study, the principles of sensor data integration and approaches to constructing hybrid SLAM architectures have been investigated, and methods for improving localization accuracy using inertial measurements have been generalized. It has been established that system efficiency is determined by adaptability to visual data degradation and the ability to compensate for inertial drift. Quantitative analysis has shown that the proposed hybrid architecture enables a reduction of inertial drift accumulation by 65–70 %, a decrease in computational latency by 60–70 %, and an improvement in localization accuracy by 6–8 times under critical conditions (reducing RMSE from approximately 1.7–1.8 m to 0.18–0.25 m). The scientific novelty lies in the systematic generalization of principles for constructing hybrid visual–inertial architectures, taking into account sensor data degradation, and in substantiating adaptive approaches to ensuring robust localization. The practical significance of the obtained results lies in their applicability to the development of navigation systems for unmanned platforms, mobile robotics, and rescue operations, where the ability to operate in real time under low or variable visibility conditions is critical.
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