# A Method for the Calibration of a LiDAR and Fisheye Camera System
## 5 Conclusions  
In this paper, a method for the calibration of a system composed of a fisheye camera and a LiDAR sensor has been proposed. The proposed framework permits obtaining the calibration parameters (i.e., the transformation matrix between the reference frames of the fisheye camera and the LiDAR sensor) without the need to project the fisheye image to a pinhole model, leading to a computationally efficient algorithm.  
The method has been tested in a simulated environment and in a real environment, where the results obtained show that the proposed method is able to calibrate the system with a high degree of accuracy. The reprojection error is used as a metric to evaluate the quality of the calibration, and the results obtained are of great importance for the development of autonomous systems that require a wide field of view. The time needed to calculate the transformation is substantially lower than the minimization method, which is a plus for the proposed method.  
The method is a relevant contribution to the field of autonomous systems as it enables the fusion of the information from a fisheye camera and a LiDAR sensor. The experimental section has demonstrated that the results obtained by the proposed calibration method permit fusing the information from the camera and LiDAR accurately despite the great distortion present in the fisheye image, obtaining a combination of visual and distance information from the environment with a large field of view.  
In the future, the method could be improved by using algorithms that can detect the corners of the rectangles in the image more accurately and in a completely automatic way so that the reprojection error can be reduced. The fusion of the information from both sensors will also enable us to perform additional developments, like semantic segmentation, object detection, object tracking, or even 3D reconstruction of the environment. Finally, we plan to compare the relative performance obtained in these tasks with this proposal compared to the case of fusing LiDAR and a pinhole camera.  
Conceptualization, A.M.; methodology, A.M.; software, A.M. and A.S.; validation, A.M.; formal analysis, A.M.; investigation, A.M.; resources, M.B., A.G. and L.P.; data curation, A.M.; writing--original draft preparation, A.M.; writing--review and editing, A.G. and L.P.; visualization, A.M.; supervision, M.B., A.G. and L.P.; project administration, M.B., A.G. and L.P.; funding acquisition, M.B., A.G. and L.P. All authors have read and agreed to the published version of the manuscript.  
MCIU/AEI/10.13039/501100011033: TED2021-130901B-I00; European Union NextGenerationEU/PRTRT: TED2021-130901B-I00; MICIU/AEI/10.13039/501100011033: PID2023-149575 OB-I00; FEDER, UE: PID2023-149575 OB-I00.  
Not applicable.  
The data presented in this study is publicly available on free repositories.  
This research work is part of the project TED2021-130901B-I00 funded by MICIU/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR. It is also part of the project PID2023-149575 OB-I00 funded by MICIU/AEI/10.13039/501100011033 and by FEDER, UE.