Calibration output

The calibration output consists of either a calibration zip file containing the following:

Sensor extrinsics
Camera intrinsics
Time offsets
Ground detection (if the robot moves in a plane on flat ground)
Wheel intrinsics (if encoders or wheel speeds were included)
Metadata

Sensor extrinsics

Filename	Description
`extrinsics.urdf`	NVIDIA Isaac Perceptor compatible URDF
`extrinsics.yaml`	YAML describing 6DoF extrinsics

The YAML includes transforms between the reference frame and the 6DoF pose of each sensor. The transforms are specified as positions [x,y,z] in meters with quaternions [x,y,z,w].

Example:

# Calibration Anywhere by Main Street Autonomy
# 
# Position xyz; Quaternions xyzw
# [ x_m, y_m, z_m, qx, qy, qz, qw]
back_2d_lidar:
  parent: "base_link"
  child: "back_2d_lidar"
  value: [-0.494047, 0.006522, 0.426849, -0.001075, 0.001938, -0.017212, 0.999849]
front_2d_lidar:
  parent: "base_link"
  child: "front_2d_lidar"
  value: [0.021299, -0.003307, 0.424852, 0.001939, 0.001071, 0.999878, 0.015468]
front_3d_lidar:
  parent: "base_link"
  child: "front_3d_lidar"
  value: [-0.234892, -0.006363, 0.527931, -0.001696, -0.000016, -0.001686, 0.999997]
chassis_imu:
  parent: "base_link"
  child: "chassis_imu"
  value: [-0.216158, 0.012443, 0.164176, -0.000206, 0.001893, 0.705067, 0.709138]
right_stereo_camera:
  parent: "base_link"
  child: "right_stereo_camera"
  value: [-0.286952, -0.166885, 0.352829, 0.005047, 0.003323, 0.707693, -0.706494]
left_stereo_camera:
  parent: "base_link"
  child: "left_stereo_camera"
  value: [-0.435905, 0.148248, 0.352080, -0.000206, 0.001893, 0.705067, 0.709138]
front_stereo_camera:
  parent: "base_link"
  child: "front_stereo_camera"
  value: [0.102732, 0.063245, 0.351200, -0.002547, -0.008137, -0.004329, 0.999954]
front_fisheye_camera:
  parent: "base_link"
  child: "front_fisheye_camera"
  value: [0.110473, -0.011563, 0.380919, -0.001953, 0.010184, -0.004450, 0.999936]

Camera intrinsics

Filename	Description
`<sensor_name>.intrinsics.yaml`	OpenCV-compatible camera intrinsics model

Each imaging sensor has a corresponding YAML file that includes a projection matrix and a distortion model. MSA calibration supports fisheye, equidistant, ftheta3, rational polynomial, and plumbob (linear) models.

Readout time is included for rolling shutter cameras.

Example:

# Calibration Anywhere by Main Street Autonomy
#
{
    image_width: 1920, 
    image_height: 1200, 
    camera_name: front_fisheye_camera, 
    camera_matrix: {rows: 3, cols: 3, data: [452.05569820004172, 0, 943.94114653517386, 0, 451.71918139761948, 600.59251441034132, 0, 0, 1]}, 
    distortion_model: equidistant, 
    distortion_coefficients: {rows: 1, cols: 5, data: [0.057062094961652564, 0.013893343356487644, -0.0020329006883655913, -0.00080049913664872227, 0]}, 
    rectification_matrix: {rows: 3, cols: 3, data: [1, 0, 0, 0, 1, 0, 0, 0, 1]}, 
    readout_time_sec: -0.0015505231518380849, 
    projection_matrix: {rows: 3, cols: 4, data: [452.05569820004172, 0, 943.94114653517386, 0, 0, 451.71918139761948, 600.59251441034132, 0, 0, 0, 1, 0]}
}

Sensor time offsets

Filename	Description
`time_offsets.yaml`	YAML describing time offsets

The YAML includes time offsets for all sensors which were timestamped in the same time domain in the input data. For instance, if all the sensors report timestamps in Unix time, there will be an entry for every sensor.

Example:

# Calibration Anywhere by Main Street Autonomy
# Time offsets are listed in seconds
#
/chassis/imu: 0
/back_2d_lidar/scan: -0.022429021
/front_2d_lidar/scan: -0.002225964
/front_3d_lidar/lidar_points: -0.17906949900000002
/front_stereo_camera/image_raw: 0.0072620950000000005
/left_stereo_camera/image_raw: 0.0076680940000000003
/right_stereo_camera/image_raw: 0.0058531150000000007
/front_fisheye_camera/image_raw: 0.0059962550000000007

Ground detection

Filename	Description
`ground.yaml`	YAML specifying transforms between the ground plane and each sensor

Example:

# Calibration Anywhere by Main Street Autonomy
# 
ground_relative_to_sensors:
  - sensor_type: lidar
    sensor_name: front_3d_lidar
    origin_sensor_frame:
      - 0.23484351741359347
      - 0.0089428245755625857
      - -0.52791281978551674
    direction_sensor_frame:
      - 3.6959477739646857e-05
      - -0.003392711107378672
      - 0.99999424405610404
  - sensor_type: camera
    sensor_name: front_stereo_camera
    origin_sensor_frame:
      - 0.062374165673735785
      - 0.34980619953554176
      - -0.10789840095531419
    direction_sensor_frame:
      - 0.0050233246870731718
      - -0.99985460047339636
      - 0.016295524578032849
  - sensor_type: camera
    sensor_name: front_fisheye_camera
    origin_sensor_frame:
      - -0.012102494474520616
      - 0.38304527129333032
      - -0.10280394618481999
    direction_sensor_frame:
      - 0.0039954112451021095
      - -0.99978496675938267
      - -0.020348388902378112

Wheel intrinsics

Filename	Description
`wheels_cal.yaml`	YAML describing wheel intrinsics

The YAML includes an axle track estimate (in meters) and corrective gain factors for either left and right wheel speeds or meters-per-tick.

Example:

# Calibration Anywhere by Main Street Autonomy
#
axle_track_m: 0.4856914300973283
left_speed_gain: 0.030691271066441622
right_speed_gain: 0.030696325397954808

Metadata

Filename	Description
`msa_metadata.tar`	Reflected metadata file (if provided in upload)

If a file named msa_metadata.tar was included in the log, it will be included in the output zip file. This file is not unpacked or analyzed by Calibration Anywhere.