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GEMstack/offboard/calibration/README.md

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1. Get camera intrinsics:
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rosrun offboard\calibration\camera_info.py # Prints intrinsic matrix
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2. Update camera_in in script with intrinsics
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3. Our script assumes data is formated as: colorx.png, lidarx.npz, depthx.tif where x is some index number. Choose x depending on what data sample you want to use for calibration.
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3. Our script assumes data is formated as: colorx.png, lidarx.npz, depthx.tif where x is some index number. x is chosen using the --index flag seen below. Set it based on what data sample you want to use for calibration.Choose x depending on what data sample you want to use for calibration.
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The script also reads the lidar_to_vehicle matrix from the gem_e4_ouster.yaml file so ensure that is up to date.
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4. Run calibration:
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**3D Alignment Check**:
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1. Use vis() function in scripts to view calibrated LiDAR/camera clouds
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2. Use --vis flag when running lidar_to_vehicle.py for ground plane/object visualization
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3. Use test_transforms.py to visualize lidar point cloud on top of the corresponding png image. Helps verify accuracy of lidar->camera.
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Usage:
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python3 test_transforms.py --data_path "path to data folder" --index INDEX_NUM
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3. Use test_transforms.py to visualize the transformed lidar point cloud to camera frame on top of the corresponding png image. Helps verify accuracy of lidar->camera.
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Usage of test_transforms.py:
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```
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python3 test_transforms.py --data_path "path to data folder" --index INDEX_NUM
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```
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Data path is the directory where lidar npz and color png files are located, index number is whichever lidar/png pair you want to evaluate. Ex. lidar1.npz color1.png where INDEX_NUM is 1 (--index 1)
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**Projection Validation**:
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1. RGB image overlaid with transformed LiDAR points (Z-buffered)

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