融合逆密度梯度和双线性插值的深海地图构建

Deep-sea Map Building Based on Combination of Inverse Density Gradient and Bilinear Interpolation

为提高深海探测机器人水下作业的效率和安全性,提出一种融合逆密度梯度聚类和双线性插值的地图构建方法。将机器人所采集的海底环境图像进行灰度化、分割和去噪预处理;对障碍物区域的图像像素进行聚类;根据探测机器人结构尺寸对聚类后二值图像进行改进双线性插值的局部膨胀处理,获得最终环境地图。2种不同环境的地图构建结果表明:传统Meanshift算法和双线性插值算法相比,融合逆密度梯度聚类和双线性插值的图像处理能够实现地图中非可行区域的确定,检出率平均提升26.1%,漏检率平均降低31.4%,该方法有效。

In order to improve the efficiency and safety of underwater operation of deep-sea exploration robot, a map construction method combining inverse density gradient clustering and bilinear interpolation is proposed. The image of seabed environment collected by the robot is pre processed by graying, segmentation and denoising;Clustering the image pixels of the obstacle area;According to the structure size of the detection robot, the binary images after clustering are locally expanded by improved bilinear interpolation to obtain the final environment map. The results of map construction in two different environments show that, compared with the traditional Meanshift algorithm and bilinear interpolation algorithm, the image processing combined with inverse density gradient clustering and bilinear interpolation realizes the determination of infeasible regions in the map, with the average detection rate increasing by 26.1% and the average missing detection rate decreasing by 31.4%, which verifies the effectiveness of this method.