Abstract: 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.

Key words: stereo matching, mars partial surface exploration, deep fusion, convolutional neural networks, normalization of matching cost