Abstract:

Traditional simultaneous localization and mapping （SLAM） methods for vehicles generally rely on the assumption of static environment， so the positional estimation accuracy may be decreased and the front-end visual odometer may even fail to track in dynamic scenes. This paper proposes a SLAM method for dynamic scenes by combining Fast-SCNN real-time semantic segmentation network and motion consistency constraints. Firstly， FAST-SCNN is used to obtain a segmentation mask of potential dynamic targets and remove the feature points to obtain a preliminary estimation of the camera position. Subsequently， based on the motion constraints and the chi-square test， the static points in the potential dynamic target are added again to further optimize the camera pose. The validation set test results show that the average pixel accuracy and mean intersection over union （mIOU） of the proposed semantic segmentation network is greater than 90%， with the processing time for 1 frame of picture is about 14.5 milliseconds， which meets the segmentation accuracy and real-time requirements of the SLAM system. Based on the public data set of TUM and real vehicle data set， the average performance improvement by using the proposed method exceeds by 80% over ORB-SLAM3 in various indicators， which significantly enhances the operating accuracy and robustness of SLAM in dynamic scenes and hence guarantees driving safety of intelligent vehicles.

Key words: intelligent vehicle, simultaneous localization and mapping, semantic segmentation, dynamic scenes, motion consistency