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<!DOCTYPE html>
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<title>README.html</title>
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<h2 id="camera-lidar-calibration-tool."><a name="camera-lidar-calibration-tool." href="#camera-lidar-calibration-tool."></a>Camera Lidar Calibration Tool.</h2><p><img src="http://progressed.io/bar/91?title=done" alt="building"></p><p>Author:xinliangzhong([email protected])</p><p><img src="results/rotation.gif" alt="demo0"></p><p><img src="results/demo.gif" alt="demo"></p><h3 id="1.description"><a name="1.description" href="#1.description"></a>1.Description</h3><p>The package is used to calibrate a 2D LiDAR or laser range finder (LRF)<br>with a monocular camera. Specficially, Hokuyo UTM-30LX have been suscessfully calibrated against a mono camera.</p><p><strong>But this approach is really a naive way, in a word, it just 3D-2D optimization problem.</strong><br><strong>So we decide to develop the new approach to calibrate the extrinsic</strong> </p><h3 id="2.prerequisites"><a name="2.prerequisites" href="#2.prerequisites"></a>2.Prerequisites</h3><p>We have tested the library in 16.04, but it should be easy to compile in other platforms.</p><h5 id="opencv"><a name="opencv" href="#opencv"></a>OpenCV</h5><p>We use <a href="http://opencv.org">OpenCV</a> to manipulate images and features. Dowload and install instructions can be found at: <a href="http://opencv.org">http://opencv.org</a>. <strong>Required at leat 2.4.3. Tested with OpenCV 3.3</strong>.</p><h5 id="eigen3"><a name="eigen3" href="#eigen3"></a>Eigen3</h5><p>Download and install instructions can be found at: <a href="http://eigen.tuxfamily.org">http://eigen.tuxfamily.org</a>. <strong>Required at least 3.1.0</strong>.</p><h5 id="ceres"><a name="ceres" href="#ceres"></a>Ceres</h5><p>Download and install instructions can be found at: <a href="http://www.ceres-solver.org/installation.html">http://www.ceres-solver.org/installation.html</a>.</p><h3 id="3.how-to-build"><a name="3.how-to-build" href="#3.how-to-build"></a>3.How to build</h3><pre><code data-origin="<pre><code>mkdir -p ws/src
cd ws/src
catkin_init_workspace
cd ../
catkin_make --pkg camera_laser_calibration
</code></pre>">mkdir -p ws/src
cd ws/src
catkin_init_workspace
cd ../
catkin_make --pkg camera_laser_calibration
</code></pre><h3 id="4.how-to-prepare-the-calibration-data"><a name="4.how-to-prepare-the-calibration-data" href="#4.how-to-prepare-the-calibration-data"></a>4.How to Prepare the calibration data</h3><h4 id="step1:-collect-laser-and-camera-data."><a name="step1:-collect-laser-and-camera-data." href="#step1:-collect-laser-and-camera-data."></a>Step1: Collect Laser and Camera data.</h4><p>We need the 3d points in laser coordination and the same points in the image.<br>you can run:</p><pre><code data-origin="<pre><code>roslaunch camera_laser_calibration collect_laser_image_data.launch
</code></pre>">roslaunch camera_laser_calibration collect_laser_image_data.launch
</code></pre><p>remember to change the path in <strong>config.yaml</strong> and <strong>*.launch</strong> files.<br>Open rqt and choose the <strong>Plungs/Configuration/Dynamic Reconfiguration</strong> and you will find the guide.<br>We combine the data in data_v2.txt with 4 cols which represents <code>x y u v</code> respectively.</p><h4 id="step2:-calibration"><a name="step2:-calibration" href="#step2:-calibration"></a>Step2: Calibration</h4><p>Modify the <strong>calibration.launch</strong> (data path), and run:</p><pre><code data-origin="<pre><code>roslaunch camera_laser_calibration calibration.launch
</code></pre>">roslaunch camera_laser_calibration calibration.launch
</code></pre><p>The result will be saved in <strong>camera_laser_calibration/data/calibration_result.txt</strong></p><h4 id="step3-show-the-reprojection-results"><a name="step3-show-the-reprojection-results" href="#step3-show-the-reprojection-results"></a>Step3 Show the reprojection results</h4><pre><code data-origin="<pre><code>roslaunch camera_laser_calibration reprojection_test.launch
</code></pre>">roslaunch camera_laser_calibration reprojection_test.launch
</code></pre><p><strong>So how to fine the 3D-2D pairs correctly?</strong><br>Here we provide a simple way:<br>Suppose your laser in the horizontal plane, and we just need to measure the height of the laser.<br>Actually the red point in the following picture is the flag that we tested in our setups.<br><img src="results/flag.jpg" alt="flag"></p><p>For the <code>x and y</code>, you can use the rviz (2D nav goal) tool to measure. The result will be show in the terminal.<br>For the <code>u and v</code> in the image, we provide a simple tool to detect corners in the rectangle your mouse selected.<br>and the data will automatically saved in <code>data/image_points.txt</code>.<br><img src="results/corner_detect.gif" alt="corner_detect"></p><h3 id="5.key-algorithm"><a name="5.key-algorithm" href="#5.key-algorithm"></a>5.Key Algorithm</h3><p>Actually it just a least square problem.</p><p><img src="https://latex.codecogs.com/gif.latex?min\sum_{i}^{N}\left&space;\|&space;uv&space;-&space;K*T_{cl}*P_{l}&space;\right&space;\|_{2}" title="min\sum_{i}^{N}\left \| uv - K*T_{cl}*P_{l} \right \|_{2}"></p><h3 id="6.show-the-reprojection-results"><a name="6.show-the-reprojection-results" href="#6.show-the-reprojection-results"></a>6.Show the reprojection results</h3><p><code>Tcl: which takes a vector from laser to camera.</code></p><p><img src="results/optimization_result.png" alt="reprojection"></p><p><img src="results/reprojection2.png" alt="reprojection"></p><h3 id="7.good-luck-to-you!"><a name="7.good-luck-to-you!" href="#7.good-luck-to-you!"></a>7.Good luck to you!</h3>
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