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Apollo 2.0 Sensor Calibration Guide

This guide introduces the Apollo Sensor Calibration Service and describes the three new calibration tools in Apollo 2.0:

  • Camera-to-Camera Calibration
  • Camera-to-LiDAR Calibration
  • Radar-to-Camera Calibration
  • IMU-to-Vehicle Calibration

About This Guide

This guide provides the following information:

  • Overview
  • Preparation
  • Using the Calibration Tools
  • Obtaining Calibration Results
  • Validation Methods and Results

Overview

The new calibration tools in Apollo 2.0 (Camera-to-Camera Calibration, Camera-to-LiDAR Calibration, and Radar-to-Camera Calibration) are provided by an onboard executable program.For LiDAR-GNSS calibration, please refer to the LiDAR-IMU calibration guide. Velodyne HDL-64 users can also use the calibration service in Apollo 1.5. The benefit in using these tools is that they reduce the amount of work that the user must do. The user only has to start the corresponding calibration program, and the calibration work is performed and completes in real time. The user can then verify the calibration results, which are provided as .yaml files.

Preparation

Download calibration tools, and extract files to $APOLLO_HOME/modules/calibration. APOLLO_HOME is the root directory of apollo repository.

Well Calibrated Intrinsics of Camera

Camera intrinsics contain focus length, principal points, distortion coefficients, and other information. Users can obtain the intrinsics from other camera calibration tools such as the ROS Camera Calibration Tools and the Camera Calibration Toolbox for Matlab. After the calibration is completed, users should convert the result to a specific yaml format file manually.

Users must ensure that the K and D data is correct:

  • K refers to the camera matrix
  • D refers to the distortion parameters

The following is an example of a camera intrinsic file:

    header: 
      seq: 0
      stamp: 
      secs: 0
        nsecs: 0
      frame_id: short_camera
    height: 1080
    width: 1920
    distortion_model: plumb_bob
    D: [-0.535253, 0.259291, 0.004276, -0.000503, 0.0]
    K: [1959.678185, 0.0, 1003.592207, 0.0, 1953.786100, 507.820634, 0.0, 0.0, 1.0]
    R: [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0]
    P: [1665.387817, 0.0, 1018.703332, 0.0, 0.0, 1867.912842, 506.628623, 0.0, 0.0, 0.0, 1.0, 0.0]
    binning_x: 0
    binning_y: 0
    roi: 
      x_offset: 0
      y_offset: 0
      height: 0
      width: 0
      do_rectify: False

It is recommended that you perform the intrinsic calibration for every single camera instead of using unified intrinsic parameters for every camera. If you follow this practice, you can improve the accuracy of the extrinsic calibration results.

Initial Extrinsic File

The tools require the user to provide an initial extrinsic value as a reference.

The following is an example of an initial extrinsic file of Camera-to-LiDAR, where translation is the shift distance between the camera and LiDAR. The rotation is the quaternion expression form of the rotation matrix.

    header:
      seq: 0
      stamp:
        secs: 0
        nsecs: 0
      frame_id: velodyne64
    child_frame_id: short_camera
    transform:
      rotation:
        y: 0.5
        x: -0.5
        w: 0.5
        z: -0.5
      translation:
        x: 0.0
        y: 1.5
        z: 2.0

NOTE: The Camera-to-LiDAR Calibration is more dependent on initial extrinsic values. A large deviation can lead to calibration failure. Therefore, it is essential that you provide the most accurate, initial extrinsic value as conditions allow.

Calibration Site

Because the Camera-to-LiDAR Calibration method is used in natual environment, a good location can significantly improve the accuracy of the calibration. It is recommended that you select a calibration site that includes objects such as trees, poles, street lights, traffic signs, stationary objects, and clear traffic lines.

Figure 1 is an example of a good choice for a calibration site:

Figure 1. Good Choice for a Calibration Site

Required Topics

Users must confirm that all sensor topics required by the program have output messages. For more information, see: How to Check the Sensor Output?

The sensor topics that the on-board program requires are listed in Tables 1, 2, and 3.

Table 1. The Required Topics of Camera-to-Camera Calibration

Sensor Topic Name Topic Feq. (Hz)
Short_Camera /apollo/sensor/camera/traffic/image_short 9
Long_Camera /apollo/sensor/camera/traffic/image_long 9
INS /apollo/sensor/gnss/odometry 100
INS /apollo/sensor/gnss/ins_stat 2

Table 2. The Required Topics of Camera-to-LiDAR Calibration

Sensor Topic Name Topic Feq. (Hz)
Short_Camera /apollo/sensor/camera/traffic/image_short 9
LiDAR /apollo/sensor/velodyne64/compensator/PointCloud2 10
INS /apollo/sensor/gnss/odometry 100
INS /apollo/sensor/gnss/ins_stat 2

Table 3. The Required Topics of Radar-to-Camera Calibration

Sensor Topic Name Topic Feq. (Hz)
Short_Camera /apollo/sensor/camera/traffic/image_short 9
INS /apollo/sensor/gnss/odometry 100
INS /apollo/sensor/gnss/ins_stat 2

Using the Calibration Tools

This section provides the following information to use the three calibration tools:

  • Commands to run each tool
  • Data collection guidelines
  • Location of the configuration file
  • Types of data output

Before you begin to use the tools, you must verify that the localization status is 56 or the calibration tools (programs) will not collect data.

Type the following command to check localization status:

rostopic echo /apollo/sensor/gnss/ins_stat

Camera-to-Camera Calibration Tool

  1. Run the Camera-to-Camera Calibration Tool using these commands:
cd /apollo/scripts
bash sensor_calibration.sh camera_camera
  1. Follow these guidelines to collect data:
  • Because the two cameras have different timestamps, they cannot be completely synchronized, so it is important to drive the vehicle very slowly when recording the data. The slow speed of the vehicle can effectively alleviate the image mismatch that is caused by the different timestamps.

  • Make sure to enable a large enough overlap of the regions of the two camera images or the tool will not be able to perform the extrinsic calibration operation.

  1. Note the location of the configuration file:
/apollo/modules/calibration/camera_camera_calibrator/camera_camera_calibrtor.conf

Table 4 identifies and describes each element in the configuration file.

Table 4. Camera-to-Camera Calibration Configuration Description

Configuration Description
long_image_topic telephoto camera image topic
short_image_topic wide-angle camera image topic
odometry_topic vehicle vodometry topic
ins_stat_topic vehicle locolization status topic
long_camera_intrinsics_filename intrinsic file of telephoto camera
short_camera_intrinsics_filename intrinsic file of wide-angle camera
init_extrinsics_filename initial extrinsic file
output_path calibration results output path
max_speed_kmh limitation of max vehicle speed, unit: km/h
  1. The types of output from the Camera-to-Camera Calibration Tool are:
  • The calibrated extrinsic file, provided as a .yaml file.
  • Validation images that include:
    • An image captured by the telephoto camera.
    • An image captured by the wide-angle camera.
    • A warp image blended with an undistorted wide-angle camera image and an undistorted telephoto camera image.

Camera-to-LiDAR Calibration

  1. Run the Camera-to-LiDAR Calibration Tool using these commands:
cd /apollo/scripts
bash sensor_calibration.sh lidar_camera
  1. Follow these guidelines to collect data:
  • Because the two cameras have different timestamps, they cannot be completely synchronized, so it is important to drive the vehicle very slowly when recording the data. The slow speed of the vehicle can effectively alleviate the image mismatch that is caused by the different timestamps.

  • Make sure that there are a certain number of (over 500) projection points in the camera image, or the tool cannot perform the extrinsic calibration operation. For this reason, this tool is only for wide angle cameras.

  1. Note the location of the saved configuration file:
/apollo/modules/calibration/lidar_camera_calibrator/camera_camera_calibrtor.conf

Table 5 identifies and describes each element in the configuration file.

Table 5. Camera-to-LiDAR Calibration Configuration Description

Configuration Description
camera_topic wide-angle camera image topic
lidar_topic LiDAR point cloud topic
odometry_topic vehicle odometry topic
ins_stat_topic vehicle localization status topic
camera_intrinsics_filename intrinsic file of camera
init_extrinsics_filename initial extrinsic file
output_path calibration results output path
calib_stop_count required stops of capturing data
max_speed_kmh limitation of max vehicle speed, unit: km/h
  1. The types of output from the Camera-to-LiDAR Calibration Tool are:
  • The calibrated extrinsic file, provided as a .yaml file
  • Two validation images that project the LiDAR point cloud onto a camera image:
    • One image is colored with depth
    • One image is colored with intensity

Radar-to-Camera Calibration

  1. Run the Radar-to-Camera Calibration Tool using these commands:
cd /apollo/scripts
bash sensor_calibration.sh radar_camera
  1. Follow this guideline to collect data:

Drive the vehicle at a low speed and in a straight line to enable the calibration tool to capture data only under this set of conditions.

  1. Note the location of the saved configuration file:
/apollo/modules/calibration/radar_camera_calibrator/conf/radar_camera_calibrtor.conf

Table 6 identifies and describes each element in the configuration file.

Table 6. Radar-to-Camera Calibration Configuration Description

Configuration Description
camera_topic wide angle camera image topic
odometry_topic vehicle odometry topic
ins_stat_topic vehicle locolization status topic
camera_intrinsics_filename intrinsic file of camera
init_extrinsics_filename initial extrinsic file
output_path calibration results output path
max_speed_kmh limitation of max vehicle speed, unit: km/h
  1. The types of output from the Radar-to-Camera Calibration tool are:
  • The calibrated extrinsic file, provided as a .yaml file
  • A validation image that includes the projection result from Radar-to-LiDAR. You need to run the radar_lidar_visualizer tool to generate the image. See [Radar LiDAR Visualizer Projection Tool](####Radar LiDAR Visualizer Projection Tool) for more information.

IMU-to-Vehicle Calibration

  1. Download the calibration tool.

  2. Start the vehicle to move before calibration. The vehicle should keep going straight at speed of 3m/s for 10s at least. There is no need to provide the intrinsic and initial extrinsic.

  3. Required topic: INS /apollo/sensors/gnss/odemetry 100Hz

  4. Run the IMU-to-Vehicle Calibration using these commands:

cd /apollo
bash scripts/sensor_calibration.sh imu_vehicle
  1. The result is saved as vehicle_imu_extrinsics.yaml in current path. Here is an example:
    header
      seq: 0
      stamp:
        secs: 1522137131
        nsecs: 319999933
      frame_id: imu
    transform:
      translation:
        x: 0.0
        y: 0.0
        z: 0.0
      rotation:
        x: -0.008324888458427
        y: -0.000229845441991
        z: 0.027597957866274
        w: 0.999584411705604
    child_frame_id: vehicle

    #pitch install error: -0.954337
    #roll install error: 0.000000
    #yaw install error: 3.163004

(Optional) Run All Calibration Tools

If necessary, users can run all calibration tools using these commands:

cd /apollo/scripts
bash sensor_calibration.sh all

Obtaining Calibration Results

All calibration results are saved under the output path in the configuration files, and they are provided in yaml format. In addition, depending on the sensor, the calibration results are stored in different folders in the output directory as shown in Table 7:

Table 7. Path of Saved Calibration Results for Each Sensor

Sensor Path for Saved Results
Short_Camera [output]/camera_params
Long_Camera [output]/camera_params
Radar [output]/radar_params

Validation Methods and Results

When the calibration is complete, the corresponding calibration result verification image is generated in the [output]/validation directory.

This section provides the background information and the corresponding validation method to use to evaluate verification images for each calibration tool.

Camera-to-Camera Calibration

  • Background Information: In the warp image, the green channel is produced from the wide-angle camera image, and the red and blue channels are produced from the telephoto camera image. Users can compare the alignment result of the warp image to validate the precision of the calibrated extrinsic parameter.

  • Validation Method:

    In the fusion area of the warp image, judge the alignment of the scene 50 meters away from the vehicle. If the images coincide completely, the extrinsic parameter is satisfactory. However, if a pink or green ghost (displacement) appears, the extrinsic parameter is in error.

    When the error is greater than a certain range (for example, 20 pixels, determined by the actual usage), you need to re-calibrate the extrinsic parameter. Under general circumstances, due to the parallax, some dislocations may occur in the horizontal with close objects, but the vertical direction is not affected. This is a normal phenomenon.

  • **Examples: **As shown in the following examples, Figure 2 meets the precision requirements of the extrinsic parameter, and Figure 3 does not.

Figure 2. Good Calibration Result for Camera-to-Camera Calibration

Figure 3. Bad Calibration Result for Camera-to-Camera Calibration

Camera-to-LiDAR Calibration

  • Background Information: In the point cloud projection images, users can see objects and signs with obvious edges and compare the alignment.
  • Validation Method: If the target is within 50 meters, its edge of point cloud can coincide with the edge of the image, and the accuracy of the calibration results can be proved to be very high. However, if there is a misplacement, the calibration results are in error. The extrinsic parameter is not available when the error is greater than a certain range (for example, 5 pixels, depending on the actual usage).
  • Examples: As shown in the following examples, Figure 4 meets the precision requirements of the extrinsic parameter, and Figure 5 does not.

Figure 4. Good Camera-to-LiDAR Calibration Validation Result

Figure 5. Bad Camera-to-LiDAR Calibration Validation Result

Radar-to-Camera Calibration

  • Background Information: To verify the extrinsic output, use the LiDAR in the system as a medium. This approach enables you to obtain:

    • The extrinsic parameter of the radar relative to the LiDAR through the extrinsic value of the radar relative to the camera

    • The extrinsic value of the camera relative to the LiDAR

      You can then draw a bird's-eye-view fusion image, which fuses the radar data and the LiDAR data in the LiDAR coordinate system. You can use the alignment of the radar data and the LiDAR data in the bird's-eye-view fusion image to judge the accuracy of the extrinsic parameter. In the fusion image, all of the small white points indicate the LiDAR point cloud, while the large green solid circles indicate radar objects.

  • Validation Method:

    The alignment of the radar object and the LiDAR data in the bird's-eye-view fusion image shows the accuracy of the extrinsic parameter. If most of the targets coincide, it is satisfactory. However, if over 40% targets (especially vehicles) do not align, it is not satisfactory and you need to re-calibrate.

  • Examples: As shown in the following examples, Figure 6 meets the precision requirements of the extrinsic parameter, and Figure 7 does not.

Figure 6. Good Camera-to-Radar Calibration Validation Result

Figure 7. Bad Camera-to-Radar Calibration Validation Result

Radar LiDAR Visualizer Projection Tool

To obtain the fusion image of the radar data and the LiDAR point cloud, the calibration process automatically (if using bash sensor_calibration.sh all) or manually (if using bash sensor_calibration.sh visualizer) calls another projection tool, the radar_lidar_visualizer. The projection tool loads the extrinsic files of the Radar-to-Camera and the Camera-to-LiDAR.

IMPORTANT: Before the projection tool starts, make sure that the two extrinsic parameters are well calibrated and exist in the specific path set in the configuration file (radar_camera_extrinsics_filename and camera_lidar_extrinsics_filename).

  1. Run the radar_lidar_visualizer program using these commands:
cd /apollo/scripts
bash sensor_calibration.sh visualizer
  1. Note the saved location of the configuration file of radar_lidar_visualizer:
/apollo/modules/calibration/radar_lidar_visualizer/conf/radar_lidar_visualizer.conf

Table 8 identifies and describes each element in the projection tool configuration file.

Table 8. Projection Tool Radar-to-LiDAR Configuration Description

Configuration File Description
radar_topic Radar data topic
lidar_topic LiDAR point cloud topic
radar_camera_extrinsics_filename Calibrated extrinsic of Radar-to-Camera
camera_lidar_extrinsics_filename Calibrated extrinsic of Camera-to-LiDAR
output_path Validation results output path
  1. Note the location of the saved validation image:

    [output]/validation