Quadcam Calibration Toolbox

A minimal, automated toolkit to calibrate a 4-camera fisheye system (e.g., OAK-FFC-4P) and generate rectification maps for stereo depth estimation of the OmniNxt system. This repo was inspired by this repo.

This toolkit streamlines the process into two steps:

1. Physical Calibration Records ROS 2 data and uses TartanCalib (Docker) to solve intrinsics and extrinsics.

2. Map Generation Analytically generates virtual stereo cameras, computes rectification, merges lookup tables, and visualizes results for verification.

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Prerequisites

1. Python Dependencies

Install the required Python packages:

pip install -r requirements.txt

2. System Dependencies

ROS 2 (Humble recommended) Source your environment:

source /opt/ros/humble/setup.bash

Docker Required for running the TartanCalib calibration solver.

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Step 0: Start Camera Driver

Before starting the calibration scripts, launch the camera driver oak_ffc_4p_driver to publish the required image topics. Make sure that your drone and your PC where you're doing the calibration have the same ROS_DOMAIN_ID to be able to subscribe to the image topic. You need to launch:

ros2 launch oak_ffc_4p_driver_ros2 oak_ffc_4p_driver_calibration.launch

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Step 1: Record & Calibrate

Run calibrate_physical.py to record a calibration sequence and solve for the physical parameters of the rig:

python3 calibrate_physical.py 

Interactive Recording

A window will open showing the camera feed (4-image grid).

Press SPACE to advance through stages:

The order or recording goes like this for easy setup: mono0, pair01, mono1, pair12, mono2, pair23, mono3, pair30.

Press Q to finish recording. The script will automatically launch Docker to run the solver.

After the calibration is done, check the pdf files in calibration_output/cam_* and make sure the reprojection error is less then 1.5 pixels. If not, you can recalibrate the camera/pair in question using the --only argument (see arguments below).

Arguments

• --out – Output directory (default: calibration_output)
• --topic – ROS 2 image topic (default: /oak_ffc_4p_driver_node/compressed)
• --uncompressed – Use if topic publishes raw sensor_msgs/Image
• --only – Calibrate specific targets only (e.g., --only mono0,pair01)
• --skip-recording – Re-calibrate using existing bags only

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Step 2: Generate Maps & Verify

Run the map generation script:

python3 generate_maps.py --width 224 --height 224 --hfov 110

Visual Verification

For each stereo pair, a window will show:

Top: Raw input pair Bottom: Rectified pair with green horizontal epipolar lines

You should verify that:

• Green lines are perfectly horizontal
• Features align across left/right views

Press any key to continue to the next pair.

Arguments

• --calib-dir – Input calibration folder (default: calibration_output)
• --out-dir – Output directory for final maps (default: final_maps)
• --width, --height – Final rectified resolution (default: 224x224)
• --hfov – Horizontal FoV for virtual stereo (default: 110°)

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Step 3: Integration

The final_maps/ directory will contain all the configuration files required by the depth estimation node.

Files Generated Per Stereo Pair

• final_rectified_to_fisheye_map_{i}_{j}.yml – OpenCV remap lookup tables
• final_map_config_{i}_{j}.yaml – Calibration metadata (intrinsics, baseline, poses)

Deploy

Copy the contents of:

final_maps/

to your depth estimation package’s config directory, e.g.:

depth_estimation_ros2/config/final_maps_224_224

TODO

Add calibration between cameras and IMU for VIO.