An Image Pre-Processing Method for Visual Simultaneous Localization and Mapping in Dynamic Environments

Abstract:

This paper proposes an image preprocessing method for visual simultaneous localization and mapping (SLAM) systems in dynamic environments， which can be easily integrated into existing visual SLAM systems to enable stable， accurate， and continuous operation in highly dynamic environments. First， it proposes a dynamic object recognition algorithm that integrates the use of semantic segmentation networks and optical flow estimation networks to robustly and accurately identify potential dynamic objects in images. Then， to detect shadows associated with dynamic objects， it proposes a shadow recognition algorithm based on region growing. Afterwards， it uses image completion techniques to fill in the gaps left by the removal of dynamic objects from the images. Finally， it combines this image preprocessing method with stereo ORB-SLAM2 and conducts experiments on the KITTI dataset， which demonstrates that the proposed image preprocessing method significantly improves the positioning accuracy of visual SLAM systems. Each module in the image preprocessing method plays an irreplaceable role.