Implementation of Stereo Matching Algorithm Based on Xavier Edge Computing Platform

In view of the existing high-precision stereo matching based on deep learning which network structure is complex, and it is difficult to deploy and run in real time on edge platform. An improved stereo matching algorithm based on RTStereoNet is proposed. Firstly, the channel attention mechanism is introduced in the matching cost aggregation stage of RTStereoNet, so that the network can adaptively enhance the extraction of effective information and reduce the ambiguity of matching. Secondly, in the disparity refinement stage of RTStereoNet, the color image is introduced to compensate for the loss of details caused by the large-scale downsampling of the network, and a lightweight disparity refinement module is constructed to expand the receptive field of the network. In addition, based on Jetson Xavier NX edge computing module, a special edge computing platform is constructed, with the help of TensorRT inference framework, the calculation support problem of special operators is solved through CUDA programming, and achieved deployment acceleration on the platform for both models before and after the improvement. The results show that after the accelerated deployment, the inference speed of the improved model can reach 30 fps on the KITTI2015 test set, and the improved model has higher accuracy than the original model.