基于双目相机的水下视觉SLAM前端改进

为使机器人能够在昏暗且模糊的水下环境中实现较为清晰的地图构建和准确的自身定位，基于Rtab-map算法对其前端进行优化。通过直方图均衡化算法对双目相机采集到的水中图像进行预处理，提高图像的亮度和细节处的清晰度。使用ORB算法提取和匹配图像特征点，采用RANSAC算法剔除图像误匹配点对，提高相机位置估计的精度。实验结果表明：使用直方图均衡化算法处理水下的模糊图像，特征匹配点对数明显增加。基于公开数据集对优化后的Rtab-map算法进行测试，所得到的多项误差明显降低。在实验室水槽条件下进行试验，验证了采用优化后的算法得到的三维点云图质量更优

Improvement of Underwater Visual SLAM Front-end Based on Stereo Camera

In order to enable the robot to achieve relatively clear map construction and accurate self-positioning in a dim and fuzzy underwater environment, the front-end was optimized based on the Rtab map algorithm. The histogram equalization algorithm was used to preprocess the underwater images acquired by the stereo camera, and the brightness of the image and the clarity of the detail were improved. The image feature points were extracted and matched by using the ORB algorithm, and the mismatched point pairs were eliminated by using the RANSAC algorithm, and the accuracy of the camera position estimation was improved. The experimental results show that when the histogram equalization algorithm is used to deal with the underwater blurred images, the feature matching point pairs increases obviously.The optimized Rtab-map algorithm was tested based on the public data set, and the obtained multiple errors were significantly reduced. The experiment was carried out under the sink condition in laboratory. It is verified that the quality of the 3D point cloud images obtained by using the optimized algorithm is better