An Effective Camera-to-Lidar Spatiotemporal Calibration Based on a Simple Calibration Target

Area of Science:

• Robotics and Computer Vision
• Sensor Fusion and Calibration

Background:

• Accurate calibration between Lidar and camera sensors is crucial for effective sensor fusion in applications like mobile mapping.
• Existing calibration methods can be complex and time-consuming, necessitating simpler and more intuitive approaches.

Purpose of the Study:

• To present a simple, intuitive, and accurate method for estimating the extrinsic (geometrical) and temporal (synchronization) calibration between Lidar and camera.
• To develop an open-source software framework for Lidar-camera calibration on the ROS platform.

Main Methods:

• Geometrical calibration achieved through 2D-to-3D point correspondences using AprilTag fiducial markers and retroreflective Lidar targets on a planar board.
• Temporal calibration performed by tracking the AprilTag and Lidar target during steady motion.
• Implementation on the Robot Operating System (ROS) platform with an open-source software release.

Main Results:

• The proposed method achieved high accuracy in determining the geometrical and temporal calibration parameters.
• Evaluation against a state-of-the-art chessboard-based method demonstrated comparable or superior accuracy.
• Successful application to a multi-camera mobile mapping system (MMS) for accurate 3D city point cloud texturing.

Conclusions:

• The developed approach offers a simple, accurate, and robust solution for Lidar-camera calibration.
• The open-source framework facilitates wider adoption and integration in robotics and autonomous systems.
• The accurate calibration enables enhanced performance in multi-camera sensor fusion applications.