An Zhe, Liu Yang
Opt Express. 2022 Dec 19;30(26):46418-46434. doi: 10.1364/OE.477750.
Two three-dimensional tracking registration methods combined with Riemannian manifold object constraints are proposed to solve problems of low accuracy and instability of three-dimensional tracking registration in sparse and complex scenes. A deep convolution neural network is used to extract three-dimensional instance objects from the location by analyzing reasons that affect registration accuracy in sparse and complex scenes. The three-dimensional tracking registration model is established according to the Riemannian manifold constraint relationship of instance objects in different states. The stability of the three-dimensional tracking registration algorithm is improved by combining inertial sensors, and cumulative error is optimized using instance object labels to improve algorithm robustness. The proposed algorithm can effectively improve the accuracy of three-dimensional tracking registration. It can improve the performance of augmented reality systems and be applied to power system navigation, medical, and other fields.
提出了两种结合黎曼流形对象约束的三维跟踪配准方法，以解决稀疏复杂场景中三维跟踪配准精度低和不稳定的问题。通过分析影响稀疏复杂场景中配准精度的原因，利用深度卷积神经网络从位置中提取三维实例对象。根据不同状态下实例对象的黎曼流形约束关系建立三维跟踪配准模型。结合惯性传感器提高三维跟踪配准算法的稳定性，并利用实例对象标签优化累积误差以提高算法鲁棒性。所提算法能有效提高三维跟踪配准的精度。它可以提高增强现实系统的性能，并应用于电力系统导航、医疗等领域。
