  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.
