Synthetic automotive LiDAR with non-systematic error and automotive LiDAR based on active stereo dataset

<p>The synthetic dataset was generated by transforming the original dataset using several methods. Each of these transformations occurs from a use case:</p><ul><li>UC1 is the original dataset obtained from [1] and represents a point cloud dataset captured by an ideal LiDAR</li><li>UC2 is a realistic point cloud dataset obtained by simulating the non-systematic error of a Velodyne HDL-64E and applying to UC1</li><li>UC3 is our approach to replace the LiDAR with an active stereo setup. Where the point cloud are captured using two cameras, operating stereoscopically, and a dot projector. The cameras are perfectly calibrated and the triangulation is always correct.</li><li>UC4 also obtains the point clouds through triangulation. However we introduced a calibration error on the right camera. The error has the value of 1 pixel and is added to every dimension of the rotation matrix of the right camera.</li><li>UC5 performs an ideal triangulation, same as UC3. &nbsp;However, in this use case, we introduce camera noise to the point clouds.</li><li>UC6 is a combination of the triangulation from UC4 and the camera noise from UC5.</li></ul><p>Additionally, the labels, images and calibration file are also in [1]. For further details, please check the dataset generation source code [2].&nbsp;</p><p>[1] https://zenodo.org/records/7184990</p><p>[2] https://github.com/RobertoGraca/Active_Stereo_Based_LiDAR</p>