Application of adaptive Monte Carlo method to evaluate pose uncertainty in monocular vision system

Due to the influence of many factors in monocular vision pose measurement, the reliability of pose measurement results needs to be evaluated by uncertainty. The difficulties of the traditional guide to the expression of uncertainty in measurement (GUM) method in actual pose evaluation are analyzed, and the adaptive Monte Carlo method (AMCM) is used to evaluate the uncertainty of pose results. According to the mathematical model of pose measurement, the factors affecting pose results are analyzed in detail. The uncertainty evaluation model is established by the error traceability method and the distribution of uncertainty components is analyzed reasonably. Both the Monte Carlo method (MCM) and AMCM are used to evaluate the uncertainty of pose results. The feasibility and effectiveness of the AMCM evaluation method are verified. The experimental results show that MCM and AMCM, as the supplement of the GUM method, can well solve the problem of pose evaluation in the actual measurement system. However, AMCM is more convenient and efficient than MCM to solve the problem of too large or too small sampling times. (C) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)