视觉里程计VIO，总结。内含自动驾驶全套学习资料

slam总结，内含自动驾驶全套学习资料

VINS-Fusion中文注释版 创新学习用。 十分感谢港科大各位老师的工作。 目前唯一注释了前端VIO部分。 相应博客 参考内容 以及一些其他的博文，在代码中也进行了注释。 基于优化的多传感器状态估计器 VINS-Fusion是基于优化的多传感器状态估计器，可为自主应用（无人机，汽车和AR / VR）实现准确的自定位。 VINS-Fusion是的扩展，它支持多种视觉惯性传感器类型（单摄像机+ IMU，立体摄像机+ IMU，甚至仅立体摄像机）。 我们还展示了一个将VINS与GPS融合的玩具示例。 特征： 支持多种传感器（立体声摄像机/单镜头摄像机+ IMU /立体声摄像机+ IMU） 在线空间校准（相机和IMU之间的转换） 在线时间校准（相机和IMU之间的时间偏移） 视觉闭环 我们是 （2019年1月日）上排名第一的开源立体声算法。 作者：香港，曹少祖，潘洁，和 影片：

In this paper, we focus on the problem of motion tracking in unknown environments using visual and inertial sensors.We term this estimation task visual-inertial odometry (VIO), in analogy to the well-known visual-odometry problem. We present a detailed study of EKF-based VIO algorithms, by comparing both their theoretical properties and empirical performance. We show that an EKF formulation where the state vector comprises a sliding window of poses (the MSCKF algorithm) attains better accuracy, consistency, and computational efficiency than the SLAM formulation of the EKF, in which the state vector contains the current pose and the features seen by the camera. Moreover, we prove that both types of EKF approaches are inconsistent, due to the way in which Jacobians are computed. Specifically, we show that the observability properties of the EKF’s linearized system models do not match those of the underlying system, which causes the filters to underestimate the uncertainty in the state estimates. Based on our analysis, we propose a novel, real-time EKF-based VIO algorithm, which achieves consistent estimation by (i) ensuring the correct observability properties of its linearized system model, and (ii) performing online estimation of the camera-to-IMU calibration parameters. This algorithm, which we term MSCKF 2.0, is shown to achieve accuracy and consistency higher than even an iterative, sliding-window fixed-lag smoother, in both Monte-Carlo simulations and real-world testing. I

HMC9的操作手册，包括连接主机、创建分区、创建VIO、创建虚拟机等信息。。

汇总-VIO_激光SLAM相关论文分类集锦.zip

KITTI数据集适配ORBSLAM3的VIO，文件是参照stereo_kitti.cc和stereo_inertial_euroc.cc改的。参数：./stereo_inertial_kitti 词汇路径 配置文件路径 图像路径（含序列和时间） IMU路径(extract) IMU时间路径。IMU时间需要和图像序列的时间对齐。

Vivado VIO IP核定制方法，Vivado仿真工程，VIO IP核定制及参数配置；

MSCKF vio SLAM融合算法, 含matlab模型源码及完整测试集

综合点线特征双目视觉惯导里程计，基于VINS-Fusion，参考PL-VIO和VINS-Fusion框架