论文标题
单眼视觉惯性大满贯算法与车轮速度异常检测结合
Monocular visual-inertial SLAM algorithm combined with wheel speed anomaly detection
论文作者
论文摘要
为了解决基于地面移动机器人的单眼视觉惯性音量计的弱可观察性，本文提出了一种单眼惯性猛击算法，结合了车轮速度异常检测。该算法使用车轮速度计的预融合方法以紧密耦合的方式将车轮速度测量添加到最小二乘问题。对于诸如滑雪和绑架之类的异常运动情况，本文基于扭矩控制采用Mecanum移动底盘控制方法。该方法使用运动约束误差来估计车轮速度测量的可靠性。同时，为了防止不正确的底盘速度测量值对机器人姿势估计负面影响，本文使用三种方法来检测异常的底盘运动，并实时分析底盘运动状态。当确定底盘运动为异常时，从状态估计方程中除去了当前框架的车轮里程表的预一体式测量，从而确保了状态估计的准确性和鲁棒性。实验结果表明，本文中该方法的准确性和鲁棒性比单眼视觉惯性里程表的准确性和鲁棒性更好。
To address the weak observability of monocular visual-inertial odometers on ground-based mobile robots, this paper proposes a monocular inertial SLAM algorithm combined with wheel speed anomaly detection. The algorithm uses a wheel speed odometer pre-integration method to add the wheel speed measurement to the least-squares problem in a tightly coupled manner. For abnormal motion situations, such as skidding and abduction, this paper adopts the Mecanum mobile chassis control method, based on torque control. This method uses the motion constraint error to estimate the reliability of the wheel speed measurement. At the same time, in order to prevent incorrect chassis speed measurements from negatively influencing robot pose estimation, this paper uses three methods to detect abnormal chassis movement and analyze chassis movement status in real time. When the chassis movement is determined to be abnormal, the wheel odometer pre-integration measurement of the current frame is removed from the state estimation equation, thereby ensuring the accuracy and robustness of the state estimation. Experimental results show that the accuracy and robustness of the method in this paper are better than those of a monocular visual-inertial odometer.
