  This paper improves visual-inertial systems to boost the localization
accuracy for low-cost rescue robots. When robots traverse on rugged terrain,
the performance of pose estimation suffers from big noise on the measurements
of the inertial sensors due to ground contact forces, especially for low-cost
sensors. Therefore, we propose \textit{Threshold}-based and \textit{Dynamic
Time Warping}-based methods to detect abnormal measurements and mitigate such
faults. The two methods are embedded into the popular VINS-Mono system to
evaluate their performance. Experiments are performed on simulation and real
robot data, which show that both methods increase the pose estimation accuracy.
Moreover, the \textit{Threshold}-based method performs better when the noise is
small and the \textit{Dynamic Time Warping}-based one shows greater potential
on large noise.
