论文标题
基于激光雷达的导航系统的机器学习
Machine Learning for LiDAR-Based Navigation System
论文作者
论文摘要
本文通过将迭代壁橱点（ICP）配准算法（ICP）配准算法和噪声适应性的卡尔曼滤波器（AKF）结合在闭环配置中，以及来自激光扫描仪和惯性测量单元（IMU）的测量结果，提出了强大的6-DOF相对导航。在这种方法中，注册的精细分组阶段与滤波器创新步骤集成在一起以进行估算校正，而滤波器估计的传播则提供了在ICP迭代循环开始时找到相应点所需的粗略比对。 ICP点匹配的收敛性通过故障检测逻辑监测，与ICP对齐误差相关的协方差由递归算法估算。事实证明，这种ICP增强功能可以提高姿势跟踪性能的鲁棒性和准确性，并在丢失跟踪时自动恢复正确的对齐方式。设计的Kalman滤波器估计器是为了识别所需的参数，例如IMU偏见和航天中心中心（COM）的位置。
This paper presents a robust 6-DOF relative navigation by combining the iterative closet point (ICP) registration algorithm and a noise-adaptive Kalman filter (AKF) in a closed-loop configuration together with measurements from a laser scanner and an inertial measurement unit (IMU). In this approach, the fine-alignment phase of the registration is integrated with the filter innovation step for estimation correction while the filter estimate propagation provides the coarse alignment needed to find the corresponding points at the beginning of ICP iteration cycle. The convergence of the ICP point matching is monitored by a fault-detection logic and the covariance associated with the ICP alignment error is estimated by a recursive algorithm. This ICP enhancement has proven to improve robustness and accuracy of the pose tracking performance and to automatically recover correct alignment whenever the tracking is lost. The Kalman filter estimator is designed so as to identify the required parameters such as IMU biases and location of the spacecraft center-of-mass (CoM).
