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Calibration and Sensor Fusion

Interactively perform calibration, estimate lidar-camera transform, and fuse data from each sensor

Most modern autonomous or semi-autonomous vehicles are equipped with sensor suites that contain multiple sensors. It is necessary to develop a geometric correspondence between these sensors, to understand and correlate output data. Rotational and translational transformations are required to calibrate and fuse data from these sensors. Fusing lidar data with corresponding camera data is particularly useful in the perception pipeline. The lidar and camera calibration (LCC) workflow serves this purpose. It uses the checkerboard pattern calibration method. To learn more, see What Is Lidar-Camera Calibration?.

Lidar Toolbox™ algorithms provide functionalities to extract checkerboard features from images and point clouds and use them to estimate the transformation between camera and lidar sensor. The toolbox also provides downstream LCC functionalities, projecting lidar points on images, fusing color information in lidar point clouds, and transferring bounding boxes from camera data to lidar data. All of these functionalities have been integrated into the Lidar Camera Calibrator app. Using the app, you can interactively calibrate the sensors.


Lidar Camera CalibratorInteractively estimate rigid transformation between lidar sensor and camera


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estimateCheckerboardCorners3dEstimate world frame coordinates of checkerboard corner points in image
detectRectangularPlanePointsDetect rectangular plane of specified dimensions in point cloud
estimateLidarCameraTransformEstimate rigid transformation from lidar sensor to camera
projectLidarPointsOnImageProject lidar point cloud data onto image coordinate frame
fuseCameraToLidarFuse image information to lidar point cloud
bboxCameraToLidarEstimate 3-D bounding boxes in point cloud from 2-D bounding boxes in image
bboxLidarToCameraEstimate 2-D bounding box in camera frame using 3-D bounding box in lidar frame