Practical Framework for Visual Positioning in Real-World Indoor Environments

초록

A visual positioning system (VPS) determines a user's six degrees-of-freedom pose by analyzing images captured by cameras. This is crucial for applications in robotics, augmented reality, and indoor navigation. Despite advancements in such technology, creating datasets and frameworks for frequently changing indoor spaces within industrial settings remains challenging. Conventional datasets are small-scale, slow to collect, and rely on inefficient stationary devices, making them unsuitable for such environments. The proposed framework is designed to address these shortcomings and for practical application. It utilizes a LiDAR-based mobile mapping system (MSS) for efficient data handling and employs a multiple-image query approach for robust localization. Additionally, the framework uses cluster-wise pose integration for multiple-image queries to address data sparsity and similarity problems in indoor localization. The results from experiments in diverse and challenging indoor spaces demonstrate that, even with extensive queries, the proposed method outperforms existing methods in localization accuracy, achieving robust performance with 96.5% of the estimated poses within a 1.0m error of the ground truth and an average error of 0.779 m.

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