Monocular visual-inertial SLAM algorithm combined with wheel speed anomaly detection

TL;DR

This paper introduces a monocular visual-inertial SLAM algorithm enhanced with wheel speed anomaly detection, improving robustness and accuracy for ground robots by handling abnormal wheel movements and sensor faults.

Contribution

It proposes a novel integration of wheel speed measurements with anomaly detection into monocular visual-inertial SLAM, addressing weak observability issues on ground robots.

Findings

Enhanced robustness over traditional monocular visual-inertial odometry.

Effective detection and exclusion of abnormal wheel speed data.

Improved accuracy demonstrated through experimental validation.

Abstract

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.