【摘要】 在复杂产品装配过程中，对增强现实设备定位是实现虚拟引导信息与装配现场实时融合的核心。传统基于标签或预先构建离线装配基体模型的定位方式，存在装配任务与视觉定位兼容性低，单一视觉定位鲁棒性和稳定性差的问题。利用多传感器融合的视觉惯性导航方式进行定位，可提高定位的精度和鲁棒性，有效提高复杂产品装配质量和效率。本文提出了一种基于点线特征融合的双目视觉惯性定位算法，对双目相机和惯性测量单元（Inertial measurement unit,IMU）进行联合标定，并通过ORB和LSD线端融合的点线特征提取匹配，采取视觉和IMU紧耦合的方式，建立基于点线特征的视觉惯性融合的位姿误差融合模型。对比VINS-Fusion、PL-VIO和本文改进IPLVIO算法，结果表明IPL-VIO算法在结构化场景下的绝对位移和旋转误差比原算法误差更小，同时结构化场景信息更加丰富，能够应用在弱纹理的AR装配现场中，为增强现实辅助装配平台提供稳定可靠的位姿数据。更多还原

【Abstract】 In the assembly process of complex products based on augmented reality, the location of augmented reality equipment is the core of the real-time integration of virtual guidance information and assembly site. The traditional positioning method based on label or pre-built offline assembly matrix model has the problems of low compatibility between assembly task and visual positioning, and poor robustness and stability of single visual positioning. The fusion of vision and inertial measurement unit(IMU) can improve the positioning accuracy and robustness, and effectively improve the assembly quality and efficiency of complex products. In this paper, a positioning algorithm based on the fusion of binocular vision and IMU is proposed. This algorithm extracts image features through point and line features. The binocular camera and IMU are jointly calibrated, and the point and line features are extracted and matched by ORB and LSD line end fusion. The pose error fusion model of visual-inertial fusion based on point and line features is established by means of tight coupling of vision and IMU. By comparing the experiments of VINS-Fusion, PL-VIO algorithms and IPL-VIO algorithm improved in this paper, the absolute displacement and rotation error of the IPL-VIO algorithm in structured scene is smaller than that of the original algorithm, and the structured scene information is more abundant, which can be applied in AR assembly field with weak texture, and provide stable and reliable pose data for augmented reality assembly platform.更多还原