论文标题
一种用于视觉惯性探望的多功能密钥帧的无结构过滤器
A Versatile Keyframe-Based Structureless Filter for Visual Inertial Odometry
论文作者
论文摘要
通过将至少摄像机和惯性测量单元（IMU）的数据融合来进行运动估计，可以在机器人技术中进行许多应用。但是，在众多的视觉惯性遗漏方法（VIO）方法中，很少有效估计设备运动，并在线校准传感器参数，以处理来自消费者传感器的数据。本文使用基于密钥帧的无结构过滤器（KSF）来解决差距。为了效率，地标不包括在过滤器的状态向量中。为了鲁棒性，KSF Associates使用关键框架的概念具有观测和管理状态变量。为了灵活性，KSF支持随时进行IMU系统错误的校准，以及每个相机的外部，内在和时间参数。通过模拟分析了传感器参数的估计器一致性和可观察性。对设计选项的敏感性，例如，使用Euroc基准测试了功能匹配方法和相机计数。在RAW TUM VI序列和智能手机数据上评估了传感器参数估计。此外，对EUROC和TUM VI序列与最近的VIO方法评估了姿势估计精度。这些测试证实，当数据包含足够的运动时，KSF可靠地校准传感器参数，并始终如一地估计运动，准确性与最近的VIO方法匹配。我们的实现在消费者笔记本电脑上使用立体声相机图像的运行量为42 Hz。
Motion estimation by fusing data from at least a camera and an Inertial Measurement Unit (IMU) enables many applications in robotics. However, among the multitude of Visual Inertial Odometry (VIO) methods, few efficiently estimate device motion with consistent covariance, and calibrate sensor parameters online for handling data from consumer sensors. This paper addresses the gap with a Keyframe-based Structureless Filter (KSF). For efficiency, landmarks are not included in the filter's state vector. For robustness, KSF associates feature observations and manages state variables using the concept of keyframes. For flexibility, KSF supports anytime calibration of IMU systematic errors, as well as extrinsic, intrinsic, and temporal parameters of each camera. Estimator consistency and observability of sensor parameters were analyzed by simulation. Sensitivity to design options, e.g., feature matching method and camera count was studied with the EuRoC benchmark. Sensor parameter estimation was evaluated on raw TUM VI sequences and smartphone data. Moreover, pose estimation accuracy was evaluated on EuRoC and TUM VI sequences versus recent VIO methods. These tests confirm that KSF reliably calibrates sensor parameters when the data contain adequate motion, and consistently estimate motion with accuracy rivaling recent VIO methods. Our implementation runs at 42 Hz with stereo camera images on a consumer laptop.
