Monocular Event Visual Inertial Odometry based on Event-corner using Sliding Windows Graph-based Optimization 基于事件相机的单目视觉惯性里程计

ist的matlab代码视觉惯性里程表 MSCKF（多状态约束扩展卡尔曼滤波器） 介绍： 用于移动平台（例如机器人）的姿态估计的视觉惯性测距法的MSCKF算法的实现和改进。 阅读以下文章： 技术细节： 本文采用的当前方法缺乏重要的统计不一致之处。 报告中将详细讨论这种不一致之处，可以根据要求提供此不一致之处。 可以在SourceCode部分中找到源代码。 如果您遇到问题，请在问题中让我知道。

视觉惯性同步硬件 作者： （ ），（ ） 1.说明 该项目旨在对摄像机和IMU进行硬件同步，以便它们都使用相同（毫秒级）的时基。 我们已经在Ubuntu16.04（ROS Kinetic）中测试了代码。 Arduino将为每个IMU测量（200 Hz）计算精确的（微秒）时间戳。 在某些时间戳（20 Hz）下，它将触发相机（通过触发线）以捕获新图像。 时间戳记和triggerCounter数据将被发送到PC（IMU节点）。 IMU节点将从Arduino接收IMU数据，并通过新的ROS TimeReference消息（主题/ imu / trigger_time）发布时间数据。 相机节点将订阅此时间数据，以为每个相机图像重建精确的时间。 因此，消息流将如下所示： IMU –> Arduino –> PC (ROS IMU node) –> ROS camera node 2.要求 2

In this tutorial, we provide principled methods to quantitatively evaluate the quality of an estimated trajectory from visual(-inertial) odometry (VO/VIO), which is the foundation of benchmarking the accuracy of different algorithms. First, we show how to determine the transformation type to use in trajectory alignment based on the specific sensing modality (i.e., monocular, stereo and visual-inertial). Second, we describe commonly used error metrics (i.e., the absolute trajectory error and the relative error) and their strengths and weaknesses. To make the methodology presented for VO/VIO applicable to other setups, we also generalize our formulation to any given sensing modality. To facilitate the reproducibility of related research, we publicly release our implementation of the methods described in this tutorial.

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

Robust Stereo Visual Inertial Odometry for Fast.pdf

小秋SLAM笔记

Combining visual and inertial measurements has become popular in mobile robotics, since the two sensing modalities offer complementary characteristics that make them the ideal choice for accurate Visual-Inertial Odometry or Simultaneous Localization and Mapping (SLAM). While historically the problem has been addressed with filtering, advancements in visual estimation suggest that non-linear optimization offers superior accuracy, while still tractable in complexity thanks to the sparsity of the underlying problem. Taking inspirat