Simultaneous Localization and Mapping (SLAM) is the process by which a mobile robot carrying specific sensors builds a map of the environment and at the same time uses this map to estimate its pose. Currently, SLAM has been proven its value and is a hot topic. However, challenges still exist: when the mobile robot stops in the process of motion and a large number of feature points in the environment move slightly, the feature matching process cannot eliminate the non-stationary feature point pairs. The introduction of a large number of outliers (non-stationary feature point pairs) seriously affects the observation process of SLAM. It directly leads to the estimation errors of mobile robot pose and the 3D features position, and further leads to keyframe trajectory drift. If there is a mechanism that allows the robot to accurately detect itself in a stop state, then the pose and map points could be locked, and the state variables could be optimized to make the system enter the positive succession. Therefore, detecting the stop status of the mobile robot is a significant work for SLAM. In this manuscript, an improved phase correlation method is proposed to solve the problem of stop detection for the autonomous driving vehicle in the dynamic street environment. After experiments, it is revealed that the stop detection is significant for the performance improvement to the state-of-the-art visual SLAM system, and the improved phase correlation method has higher stop detection accuracy than the conventional phase correlation in various scenarios.
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