TY - GEN
T1 - Coupled
T2 - 12th Mexican Conference on Pattern Recognition, MCPR 2020
AU - Montoya, Omar
AU - Icasio, Octavio
AU - Salas, Joaquín
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - LiDARs and cameras are two widely used sensors in robotics and computer vision, particularly for navigation and recognition in 3D scenarios. Systems combining both may benefit from the precise depth of the former and the high-density information of the latter, but a calibration process is necessary to relate them spatially. In this paper, we introduce COUPLED, a method that finds the extrinsic parameters to relate information between them. The method implies the use of a setup consisting of three planes with charuco patterns to find the planes in both systems. We obtain corresponding points in both systems through geometric relations between the planes. Afterward, we use these points and the Kabsch algorithm to compute the transformation that merges the planes between both systems. Compared to recent single plane algorithms, we obtain more accurate parameters, and only one pose is required. In the process, we develop a method to automatically find the calibration target using a plane detector instead of manually selecting the target in the LiDAR frame.
AB - LiDARs and cameras are two widely used sensors in robotics and computer vision, particularly for navigation and recognition in 3D scenarios. Systems combining both may benefit from the precise depth of the former and the high-density information of the latter, but a calibration process is necessary to relate them spatially. In this paper, we introduce COUPLED, a method that finds the extrinsic parameters to relate information between them. The method implies the use of a setup consisting of three planes with charuco patterns to find the planes in both systems. We obtain corresponding points in both systems through geometric relations between the planes. Afterward, we use these points and the Kabsch algorithm to compute the transformation that merges the planes between both systems. Compared to recent single plane algorithms, we obtain more accurate parameters, and only one pose is required. In the process, we develop a method to automatically find the calibration target using a plane detector instead of manually selecting the target in the LiDAR frame.
UR - http://www.scopus.com/inward/record.url?scp=85087278701&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-49076-8_20
DO - 10.1007/978-3-030-49076-8_20
M3 - Contribución a la conferencia
AN - SCOPUS:85087278701
SN - 9783030490751
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 209
EP - 218
BT - Pattern Recognition - 12th Mexican Conference, MCPR 2020, Proceedings
A2 - Figueroa Mora, Karina Mariela
A2 - Anzurez Marín, Juan
A2 - Cerda, Jaime
A2 - Carrasco-Ochoa, Jesús Ariel
A2 - Martínez-Trinidad, José Francisco
A2 - Olvera-López, José Arturo
PB - Springer
Y2 - 24 June 2020 through 27 June 2020
ER -