Visual Loop Closure Detection With Instance Segmentation and Image Inpainting in Dynamic Scenes Using Wearable Camera

Abstract

Dynamic interference is an important factor that affects the accuracy of loop closure detection in visual SLAM. Traditional methods that remove the feature points in dynamic regions are prone to insufficient image features, which will decrease the reliability of loop closure detection. To overcome this problem, a novel loop closure detection approach based on image inpainting and feature selection is proposed. A dynamic object instance segmentation guided image inpainting network is utilized to segment and repair dynamic regions in scene images. To avoid the negative influence of image inpainting, an image quality evaluation network is used to confine the extraction of Superpoint features only in the properly inpainted patches. Therefore, valid Superpoint features only in the areas with high inpainting qualities are selected as the input of the Bag of Words model for loop-closure detection. We tested the proposed method on both the KITTI dataset and a wearable camera system. Experimental results indicate that the proposed method improves the accuracy of loop closure detection in dynamic scenes.