LiDAR-Camera Data Fusion

Fusing LiDAR data, which provides depth information, with camera images, which capture color information, opens up new possibilities for perception and understanding of the environment.

Camera Calibration

Types of parameters in camera sensor:

1. Intrinsic Parameters

It allows mapping between pixel coordinates (2D image plane coordinates) and camera coordinates in the image frame (3D camera coordinates).E.g. optical center, focal length, and radial distortion coefficients of the lens.

2. Extrinsic Parameters

It describes the orientation and location of the camera. This refers to the rotation and translation of the camera with respect to some world coordinate system i.e. mapping between 3D camera coordinates and 3D world coordinates.

Camera calibration is the process of estimating the intrinsic parameters of the camera. These parameters describe the camera’s internal characteristics and play a vital role in rectifying camera images. Here’s how you can calibrate your camera:

Step 1: Calibration Target Choose a calibration target, such as a checkerboard pattern or a specialized calibration grid, and place it within the camera’s field of view during image capture.

Step 2: Image Capture Capture a set of calibration images (as shown in the figure below), ensuring that the calibration target appears in different positions and orientations throughout the images.

Step 3: Feature Extraction Extract features from the calibration images using algorithms like corner detectors. These features serve as reference points for calibration.

Step 4: Feature Matching Match the extracted features across the calibration images to establish correspondences, which will be used for parameter estimation.

Step 5: Calibration Algorithm Apply a camera calibration algorithm, such as Zhang’s or Tsai’s method, to estimate the intrinsic parameters based on the feature correspondences.

Step 6: Optimization Optionally, refine the calibration results using optimization techniques to minimize errors and improve accuracy.

Step 7: Intrinsic Parameter Estimation The output of the camera calibration process is a set of intrinsic parameters, including focal length, principal point, and lens distortion parameters.

LiDAR-Camera Extrinsic Calibration:

In addition to camera calibration, we need to determine the extrinsic parameters that describe the relative pose between the LiDAR sensor and the camera. Here’s how you can perform LiDAR-camera extrinsic calibration:

Step 1: Data Acquisition Capture synchronized data from both the LiDAR sensor and the camera, ensuring accurate timestamping or synchronization.

Step 2: Calibration Target and Feature Extraction Include a calibration target with known geometry in the scene. Extract features from the calibration target in both the LiDAR and camera data.

Step 3: Feature Correspondences Match the extracted features between the LiDAR data and the camera images to establish correspondences.

Step 4: Pose Estimation Use the feature correspondences to estimate the transformation between the LiDAR sensor and the camera, typically involving rotation and translation components.

Step 5: Optimization Refine the initial pose estimation by minimizing the reprojection error, which measures the discrepancy between observed feature correspondences and the estimated pose.

Step 6: Extrinsic Parameters The resulting optimized transformation matrix provides the extrinsic parameters, representing the relative pose between the LiDAR sensor and the camera.

Coordinate Alignment and Fusion:

With both the intrinsic and extrinsic parameters known, we can align the LiDAR data with the camera images and fuse them together. This involves projecting the 3D LiDAR points onto the 2D camera image plane using the camera projection matrix. By combining the aligned LiDAR data and camera images, we can create a comprehensive representation of the environment.

Fusion of camera and LiDAR can be done in two ways — fusion of data or fusion of the results.

Data Fusion

Fusion of data is the overlapping of the camera image and LiDAR point cloud so that we get depth information for the pixels in the camera image.

This fusion requires finding the intersection of the FOV’s of the LiDAR and camera and then assigning the remaining points in the point cloud to corresponding pixel in the image. This output then could be upsampled to obtain depth values for all the pixels in the image.

Output Fusion

Fusion of results is where, say we do object detection in the camera image and in the LiDAR point cloud separately, and fuse the results to increase our confidence.

We process the camera image separately, get an output and verify against the processed LiDAR output or vice-versa. This is a very useful fusion method since it can help in increasing the reliability of a system.

The fusion of LiDAR data onto camera images has numerous applications, including autonomous driving, robotics, and augmented reality. By combining depth information from LiDAR with color information from cameras, we can enhance perception, object detection, and scene understanding in various fields.

By following the step-by-step process of camera calibration and LiDAR-camera extrinsic calibration, you can accurately align the two datasets and create a combined representation. Remember, accurate calibration and alignment are essential for reliable fusion and robust perception.