This paper presents Lidar-based Simultaneous Localization and Mapping (SLAM) for autonomous driving vehicles. Fusing data from landmark sensors and a strap-down Inertial Measurement Unit (IMU) in an adaptive Kalman filter (KF) plus the observability of the system are investigated. In addition to the vehicle's states and landmark positions, a self-tuning filter estimates the IMU calibration parameters as well as the covariance of the measurement noise. The discrete-time covariance matrix of the process noise, the state transition matrix, and the observation sensitivity matrix are derived in closed-form making them suitable for real-time implementation. Examining the observability of the 3D SLAM system leads to the conclusion that the system remains observable upon a geometrical condition on the alignment of the landmarks.

翻译：本文介绍了自动驾驶车辆的基于Lidar的同步本地化和绘图(SLAM),从具有适应性的Kalman过滤器(KF)和该系统的可观测性等具有适应性的Kalman过滤器(IMU)中从地标感应传感器和系紧性惰性测量股(IMU)中收集数据,并调查该系统的可观测性,除了该车辆的状态和地标位置外,自调过滤器还估计了IMU的校准参数以及测量噪音的共差性,过程噪音的离散时共变矩阵、州过渡矩阵和观测敏感矩阵是用封闭式模型衍生出来的,使其适合实时执行。审查3D SLAM系统的可观测性,得出以下结论,即该系统在对地标进行校准的几何条件下仍然可以观测。