一种融合深度信息的火星局部地表图像立体匹配方法

A Stereo Matching Method for Mars Surface Image Fused with Depth Information

针对火星局部地表形貌原始自然、色彩单一和纹理相似度高难以实现双目精确定位的问题,提出一种融合深度信息的火星局部地表图像立体匹配方法.利用空间金字塔特征提取模块聚合不同尺度和位置的上下文信息,然后通过分层立体匹配架构构建多尺度的匹配代价卷,用条件代价卷归一化代替批量归一化层,在立体匹配网络的代价正则化阶段以深度信息为条件调制匹配代价卷特征,从而降低计算量,提升推理速度,并生成高精度的视差图.最终利用感兴趣目标的视差值并结合相机的基线参数,得到目标点在指定坐标系下的三维坐标从而实现定位任务.在火星模拟场数据集上的视差图达到了三像素误差小于0.017%,通过与GCNet+CCVNorm等方法的结果进行比较,表明所提出方法在火星局部地表下的优势.

Due to the problem of the original natural partial surface of Mars,the single color and the high texture similarity,it is difficult to achieve accurate positioning of the binoculars.We propose a stereo matching method of Martian partial surface image fused with depth information.The spatial pyramid feature extraction module is used to aggregate context information of different scales and locations,and then a multi-scale matching cost volume is constructed through a hierarchical stereo matching architecture,and the batch normalization layer is replaced by conditional cost volume normalization.In the cost regularization stage of the stereo matching network,the depth information is used as the condition to modulate the cost volume features,thereby reducing the amount of calculation,improving the inference speed,and generating a high-precision disparity map.Finally,combining the disparity value of the target and the camera parameters,the three-dimensional coordinates of the target point are obtained in the specified coordinate system to realize the positioning task.The disparity map on the Mars simulation dataset achieves a three-pixel error of less than 0.017%,and the comparison with GCNet+CCVNorm and other methods shows the advantages of the proposed method in the Martian partial surface scene.