机器学习在LiDAR-Based导航系统中的应用
Machine Learning for LiDAR-Based Navigation System
摘要：
本文提出了一种鲁棒的6自由度相对导航相结合的迭代最近点（ICP）注册算法和噪声自适应卡尔曼滤波器（AKF）在一个闭环配置与测量从激光扫描仪和惯性测量单元（IMU）。在该方法中，配准的精细对准阶段与用于估计校正的滤波器创新步骤集成，而滤波器估计传播提供在ICP迭代周期开始时找到对应点所需的粗略对准。ICP点匹配的收敛由故障检测逻辑监控，并且与ICP对准误差相关联的协方差由递归算法估计。这种ICP增强已被证明可以提高姿态跟踪性能的鲁棒性和准确性，并在跟踪丢失时自动恢复正确的对齐。设计了卡尔曼滤波器估计器，以辨识所需的参数，如惯性测量单元的偏差和航天器质心（CoM）位置。
英文摘要：
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).
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