Satellite Images Help Track a Vehicle

Researchers from the Toyota Technological Institute at Chicago (TTIC) and Carnegie Mellon University developed a deep learning-based method that locates a ground vehicle by using satellite imagery as the only prior knowledge of the environment.

Knowing the exact location of a vehicle is critical for autonomous cars, and currently GPS systems are being used which the researchers claim suffer from limited precision and are sensitive to multipath effects – such as in “urban canyons” formed by tall buildings.

Using TITAN X GPUs, CUDA, and Keras with the Theano deep learning framework, the researchers multi-view neural network learns to match ground-level images with their corresponding satellite view. For training, they used ground-level images from the KITTI dataset collected from a moving vehicle and paired them with the matching satellite image.

A visualization of their network architecture that consists of two independent convolutional neural networks (CNNs) that take as input ground-level and satellite images. Each CNN is an adaptation of VGG-16 CNNs in which mid-level conv4-1 features are downsampled and combined with the output of the last max-pooling layer as the high-level features via summation. The resulting outputs are then used as a measure of distance between ground-level and satellite views.

The next step for the work is to adapt their model so it is able to tolerate more severe appearance variations like seasonal changes.

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About Brad Nemire

Brad Nemire
Brad Nemire is on the Developer Marketing team and loves reading about all of the fascinating research being done by developers using NVIDIA GPUs. Reach out to Brad on Twitter @BradNemire and let him know how you’re using GPUs to accelerate your research. Brad graduated from San Diego State University and currently resides in San Jose, CA. Follow @BradNemire on Twitter