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a dash-camera (experimental) calibration method for self-driving applications

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Motivation

The devices that run openpilot are not mounted perfectly. The camera is not exactly aligned to the vehicle. There is some pitch and yaw angle between the camera of the device and the vehicle, which can vary between installations. Estimating these angles is essential for accurate control of the vehicle. More info can be found in this readme.

Methodology

  1. Feature extraction from video using Canny edge detection:

    • cropped; grayscale; Gaussian (3,3) blur
    • Otsu's method for Canny thresholds
    • output is sparse array (per training video): shape (1200, 28618)
  2. Sequential training on RNN:

    • input tensor: shape (1, 1, 28618) (batch, time_step, input_size)
    • hidden state tensor: shape (1, 1, 28618) (n_layers, batch, hidden_size)
    • prediction tensor: shape(1, 1, 2) (batch, time_step, output_size)
    • Pytorch Adam optimizer; MSELoss objective (squared L2 norm)
    • 80/20 train test split

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a dash-camera (experimental) calibration method for self-driving applications

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