Abstract: This paper proposes a real-time location and mapping (SLAM) technology that combines region detection and semantic segmentation. The precision of inter-frame pixel matching in the visual odometer (VO) process is realized by introducing high-precision image descriptor SIFT. In order to reduce the computational complexity caused by the introduction operation, we design a real-time region detection algorithm to detect the region of interest (ROI) between adjacent frames, so that the SIFT descriptors are extracted and matched only in the ROI region. At the same time, in order to improve the accuracy of the bundle adjustment (BA) and reduce the cumulative error, the paper combines the semantic information. The semantic map is implemented by a real-time semantic segmentation algorithm. Compared with the original SLAM scheme, the proposed method can improve the accuracy of SLAM and meet the requirements of real-time localization and mapping. Finally, we verify the effect of the scheme in the power operation scenario.

Key words: SLAM, area detection, semantic segmentation, visual odometer, computer vision