A Visual-SLAM based Line Laser Scanning System using Semantically Segmented Images

Integration of the line laser scanning system with visual SLAM for 3D mapping is conceptually attractive yet facing the difficulty with processing projected line laser, which is not only hard to be extracted from images captured under natural light, but also disrupts the feature tracking procedure in visual SLAM. This paper proposes a method of segmenting the target object and extracting the laser line to build an accurate and realistic 3D model by using a semantic segmentation method. First, we introduce adaptive thresholds for the recognized objects to solve the laser extraction problem. Second, we discard the extracted image features in the laser area for better pose estimation of visual SLAM. Finally, we complement the surface of lasers with the color information in the related objects of 3D mapping. In our experiments, we show that the proposed method can produce a dense colored 3D mapping and has higher performance than the traditional visual SLAM based laser scanning system.