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面向工业场景的增强现实区域识别方法

Augmented Reality Area Recognition Method for Industrial Scenarios

  • 摘要: 目前, 工业场景中存在多相似目标同时出现在相机视野的情况. 针对现有增强现实方法无法区分多相似目标的问题, 提出面向工业场景的增强现实区域识别方法. 首先利用深度相机完成工厂区域的三维注册, 构建包含物体自然特征和背景特征的区域目标; 然后设计基于双地图重加载策略的增强现实系统框架, 实现虚拟物体的位姿设定与跨平台可视化; 最后结合位姿图优化和词袋法实现双线程计算的强鲁棒性重定位算法, 完成对区域目标的识别与当前相机位姿的计算. 在TUM数据集和搭建的真实工业场景下的实验结果表明, 重定位位姿均方根绝对轨迹误差平均值为0.076, 帧率超过60 帧/s, 并区别出同视野下的多相似目标; 所提方法精确度高、鲁棒性强、实时性好, 可有效地扩展增强现实应用场景.

     

    Abstract: At present, the industrial scenarios may have multiple similar targets in the camera field of view at the same time. Aiming at the existing augmented reality methods that cannot distinguish these targets, the augmented reality area recognition method is proposed. Firstly, the depth camera is used for completing the 3D registration of the factory area and constructing the area targets that include natural features as well as background features. Secondly, a dual map reloading strategy is designed to realize the position setting of the virtual object and cross-platform visualization. Finally, the robust relocation algorithm combined with the pose graph and bag-of-words, is used to implement the identification of the area target. The experimental results tested in the TUM dataset and real scene show that the average value of root-mean-square absolute trajectory error is 0.076, the frame rate exceeds 60, and multiple similar targets under the same field of view are distinguished. The method has high accuracy and good real-time performance, can effectively expand the application scenarios of augmented reality.

     

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