  Visual localization plays a critical role in the functionality of low-cost
autonomous mobile robots. Current state-of-the-art approaches for achieving
accurate visual localization are 3D scene-specific, requiring additional
computational and storage resources to construct a 3D scene model when facing a
new environment. An alternative approach of directly using a database of 2D
images for visual localization offers more flexibility. However, such methods
currently suffer from limited localization accuracy. In this paper, we propose
an accurate and robust multiple checking-based 3D model-free visual
localization system to address the aforementioned issues. To ensure high
accuracy, our focus is on estimating the pose of a query image relative to the
retrieved database images using 2D-2D feature matches. Theoretically, by
incorporating the local planar motion constraint into both the estimation of
the essential matrix and the triangulation stages, we reduce the minimum
required feature matches for absolute pose estimation, thereby enhancing the
robustness of outlier rejection. Additionally, we introduce a multiple-checking
mechanism to ensure the correctness of the solution throughout the solving
process. For validation, qualitative and quantitative experiments are performed
on both simulation and two real-world datasets and the experimental results
demonstrate a significant enhancement in both accuracy and robustness afforded
by the proposed 3D model-free visual localization system.
