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The frame rates of most 3D LIDAR sensors used in intelligent vehicles aresubstantially lower than current cameras installed in the same vehicle. Thisresearch suggests using a mono camera to virtually enhance the frame rate ofLIDARs, allowing the more frequent monitoring of dynamic objects in thesurroundings that move quickly. As a first step, dynamic object candidates areidentified and tracked in the camera frames. Following that, the LIDARmeasurement points of these items are found by clustering in the frustums of 2Dbounding boxes. Projecting these to the camera and tracking them to the nextcamera frame can be used to create 3D-2D correspondences between differenttimesteps. These correspondences between the last LIDAR frame and the actualcamera frame are used to solve the PnP (Perspective-n-Point) problem. Finally,the estimated transformations are applied to the previously measured points togenerate virtual measurements. With the proposed estimation, if the egomovement is known, not just static object position can be determined attimesteps where camera measurement is available, but positions of dynamicobjects as well. We achieve state-of-the-art performance on large publicdatasets in terms of accuracy and similarity to real measurements.

Virtually increasing the measurement frequency of LIDAR sensor utilizing a single RGB camera