Autonomous vehicle technology is rapidly advancing (see “Summary”). A key enabling factor is the advancing capabilities and declining cost of computing and sensing systems that enable sensor fusion for awareness of the vehicle’s state and surroundings (see “Nontechnical Article Summary”). For control purposes, the vehicle’s state must be estimated accurately, reliably, at a sufficiently high sample rate, and with a sufficiently high bandwidth. For systems with a high bandwidth, these requirements are often achieved by an aided inertial navigation system (INS) (see “Aided Inertial Navigation System History”) [1], [2], [3], [4], [5], [6]. An INS integrates data from an inertial measurement unit (IMU) through a kinematic model at the high sampling rate of the IMU to compute the state estimate. An aided INS corrects this state estimate using data from aiding sensors [for example, vision, lidar, radar, and global navigation satellite systems (GNSS)]. State estimation by sensor fusion may be accomplished using a variety of methods: Kalman filter (KF) [7], [8], [9], [10], [11], extended KF (EKF) [12], [13], [14], [15], unscented KF (UKF) [16], [17], [18], particle filter (PF) [19], [20], [21], and maximum a posteriori (MAP) optimization [22], [23], [24], [25], [26], [27].

中文翻译：

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惯性测量单元误差建模教程：具有实时传感器校准的惯性导航系统状态估计


自动驾驶汽车技术正在迅速发展（参见“摘要”）。一个关键的推动因素是计算和传感系统的能力不断进步，成本不断下降，这些系统能够通过传感器融合来感知车辆的状态和周围环境（参见“非技术文章摘要”）。出于控制目的，必须以足够高的采样率和足够高的带宽准确、可靠地估计车辆的状态。对于高带宽的系统，这些要求通常通过辅助惯性导航系统（INS）来实现（参见“辅助惯性导航系统历史”）[1]、[2]、[3]、[4]、[5] ，[6]。 INS 通过运动学模型以 IMU 的高采样率集成来自惯性测量单元 (IMU) 的数据，以计算状态估计。辅助 INS 使用辅助传感器 [例如视觉、激光雷达、雷达和全球导航卫星系统 (GNSS)] 的数据来纠正此状态估计。传感器融合的状态估计可以使用多种方法来完成：卡尔曼滤波器（KF）[7]、[8]、[9]、[10]、[11]、扩展 KF（EKF）[12]、[13 ]、[14]、[15]、无味 KF (UKF) [16]、[17]、[18]、粒子滤波器 (PF) [19]、[20]、[21] 和最大后验概率 (MAP ）优化[22]、[23]、[24]、[25]、[26]、[27]。