This dissertation presents a robust method for 6DoF position estimation under impaired visual conditions utilizing a minimum 4-point Perspective-n-Point (P4P) solver designed for tetrahedral targets. Using SO(3) × R 3 instead of SE(3), the method uses a Lie group-based formulation to discriminate between rotation and translation, thereby enabling computationally efficient, resource-conscious op- optimization while preserving correct geometric behavior. Designed using the contemporary C++17 library ShomerTarget, the solver is analytically formulated and assessed under pragmatic robotic conditions. Particularly in low-light and high-dynamic environments, experiments on embedded systems, UAVs, and NASA’s Astrobee show that the proposed solver attains enhanced accuracy compared to conventional conic-based and faster runtime than automatic differentiation solvers. Furthermore, the system is compatible with hybrid VINS, optimization-based (Astrobee), and filter-based (MSCKF) pipelines, enhancing resistance to motion blur and texture degradation. This work offers an open-source, modular, efficient P4P solver architecture suitable for real-time localization under visual deterioration.
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