Visual simultaneous localization and mapping algorithm of coal mine underground considering image enhancement

The visual simultaneous localization and mapping (SLAM) algorithm based on the feature point method has certain applications in coal mines. However, due to factors such as uneven lighting, variable lighting, and alternating light and dark areas, the image quality is poor and texture information is lacking. This results in low precision of feature extraction and matching in the front end of visual SLAM. The problem of tracking loss is prone to occur, which affects the positioning precision and mapping effect of the visual SLAM algorithm. This study proposes a visual SLAM algorithm of coal mine underground considering image enhancement. The overall performance of visual SLAM is improved through image enhancement processing. Retinex algorithm based on improved bilateral filter is used to enhance the coal mine underground image. The original RGB image is converted to HSI color space, and the improved bilateral filter replaces the Gaussian filter of the traditional Retinex algorithm as the central surrounding function. After the image reflection component is estimated, it is converted to RGB color space to obtain the final enhanced image. Retinex algorithm based on improved bilateral filter is introduced into the classical ORB-SLAM2 algorithm framework for pose estimation and mapping. Based on the data collection platform of the wheeled mine-used robot, the visual SLAM algorithm considering image enhancement is tested in the roadway environment of coal mine underground. The results show that, compared with the traditional Retinex algorithm, the coal mine image enhanced by the Retinex algorithm based on improved bilateral filter does not show obvious whitening and halo, and the image quality is improved. Compared with the ORB-SLAM2 algorithm, the visual SLAM algorithm considering image enhancement improves the quality and quantity of feature matching. It has a higher degree of overlap between estimated trajectories and real trajectories. It reduces the mean absolute trajector error by 76.2%. It establishes a more realistic and accurate 3D dense point cloud map of underground roadway.