Company Filing History:
Years Active: 2025
Title: Innovations by Sun Jae Jeong
Introduction
Sun Jae Jeong is a notable inventor based in Hwaseong-si, South Korea. He has made significant contributions to the fields of camera and lidar calibration technologies. With a total of 2 patents, his work is recognized for its innovative approaches to enhancing automotive technology.
Latest Patents
Sun Jae Jeong's latest patents include a method for the calibration of camera and lidar, as well as a method for the calibration of lidar and IMU. The first patent proposes a calibration method utilizing a calibration board with a specific pattern. This method involves extracting an image frame captured by a camera and point cloud data from a lidar. It identifies feature points on the calibration board and performs calibration based on these points. This technology was developed with support from the Ministry of Trade, Industry and Energy and the Korea Planning and Evaluation Institute of Industrial Technology.
The second patent focuses on calibrating a lidar and an IMU using features from specific objects in point cloud data. This method includes placing lidar-acquired point cloud data into a predefined world coordinate system, extracting a calibration region, and identifying objects within that region. Calibration is then performed by fitting the point cloud data to a pre-stored model. This innovation was supported by the Ministry of Land and KAIA.
Career Highlights
Sun Jae Jeong is currently employed at Mobiltech, where he continues to develop cutting-edge technologies in the automotive sector. His work is characterized by a strong emphasis on improving the accuracy and reliability of autonomous driving systems.
Collaborations
Sun collaborates with Jae Seung Kim, contributing to the advancement of their projects and innovations in the field.
Conclusion
Sun Jae Jeong's contributions to camera and lidar calibration technologies demonstrate his commitment to innovation in the automotive industry. His patents reflect a deep understanding of the complexities involved in autonomous driving systems.