Approach for Extrinsic Calibration of a Light Detection and Ranging Sensor and a Monocular Camera Using Bounding Boxes

Sensor calibration plays an important role in determining overall navigation accuracy of an autonomous vehicle (AV). Calibrating the AV’s perception sensors, typically, involves placing a prominent object in a region visible to the sensors and then taking measurements to further analyses. The analysis involves developing a mathematical model that relates the AV’s perception sensors using the measurements taken of the prominent object. The calibration process has multiple steps that require high precision, which tend to be tedious and time-consuming. Worse, calibration has to be repeated to determine new extrinsic parameters whenever either one of the sensors move. Extrinsic calibration approaches for LiDAR and camera depend on objects or landmarks with distinct features, like hard edges or large planar faces that are easy to identify in measurements. The current work proposes a method for extrinsically calibrating a LiDAR and a forward-facing monocular camera using 3D and 2D bounding boxes. The proposed algorithm was tested using the KITTI dataset and experimental data. The rotation matrix is evaluated by calculating its Euler angles and comparing them to the ideal Euler angles that describe the ideal angular orientation of the LiDAR with respect to the camera. The comparison shows that calibration algorithm’s rotation matrix is approximately close to both the ideal and the KITTI dataset rotation matrices. The corresponding translation vector is shown to be close to expected values as well. The results from the experimental data were evaluated and verified by projecting cluster measurements of the prominent objects on to corresponding images.