Distributed cognition based localization for AR-aided collaborative assembly in industrial environments

Abstract The existing (augmented reality) AR-aided assembly is highly associated with AR devices, which mainly provides guidance for one operator, and it is hard to share augmented assembly instructions for large-scale products which require multiple operators working together. To address this problem, the paper proposes a distributed cognition based localization method for AR-aided collaborative assembly. Firstly, a scene cognition using multi-view acquisition about industrial environments is performed with incremental modeling in advance, providing the foundation for the subsequent pose estimate of multi-AR clients. Then, based on feature extracting and matching against the pre-built shop floor model, a pose recovery of AR-aided system is derived from different views of AR operators in a global coordinate system, followed by a distributed motion tracking with the complementary features of visual and inertial data, resulting in a co-located collaborative AR instruction for assembly. Finally, experiments are carried out to validate the proposed method, and experimental results illustrate that the proposed method can achieve distributed cognition-based localization accurately and robustly. Therefore, shared visual communications among multiple operators are synchronized, and assembly status is aware by all the operators.