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Payload
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{  
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   "reference_id":"001",
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   "data":{
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     "sensor_data": {
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      "frames": [
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        {
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          "frame_id": "frame001",
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          "ego_pose" : {
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            "position": {
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              "x": 0,
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              "y": 0,
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              "z": 0
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            },
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            "heading": {
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              "w": 1,
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              "x": 0,
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              "y": 0,
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              "z": 0
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            }
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          },
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          "sensors" : [
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            {
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              "sensor_id": "lidar",
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              "data_url": "https://s3.amazonaws.com/example-bucket/lidar/frame001.pcd",
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              "sensor_pose": {
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                "position": {
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                  "x": 0.1,
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                  "y": 0.05,
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                  "z": 0.4
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                },
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                "heading": {
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                  "w": 0.847,
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                  "x": -0.002,
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                  "y": -0.504,
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                  "z": 0.168
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                }
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              }
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            },
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            {
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              "sensor_id": "cam-1",
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              "data_url": "https://s3.amazonaws.com/example-bucket/cam-1/frame001.png",
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              "sensor_pose": {
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                "position": {
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                  "x": 0.01,
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                  "y": 0.1,
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                  "z": 0.1
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                },
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                "heading": {
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                  "w": 0.002,
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                  "x": 0.847,
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                  "y": -0.168,
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                  "z": 0.504
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                }
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              }
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            }
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          ]
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        },
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        {
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          "frame_id": "frame002",
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          "ego_pose" : {
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                "position": {
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                  "x": 0.1,
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                  "y": 0.05,
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                  "z": 0.4
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                },
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                "heading": {
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                  "w": 0.847,
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                  "x": -0.002,
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                  "y": -0.504,
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                  "z": 0.168
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                }
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              },
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          "sensors" : [
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            {
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              "sensor_id": "lidar",
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              "data_url": "https://s3.amazonaws.com/example-bucket/lidar/frame002.pcd",
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              "sensor_pose": {
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                "position": {
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                  "x": 0,
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                  "y": 0,
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                  "z": 0
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                },
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                "heading": {
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                  "w": 1,
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                  "x": 0,
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                  "y": 0,
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                  "z": 0
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                }
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              }
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            },
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            {
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              "sensor_id": "cam-1",
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              "data_url": "https://s3.amazonaws.com/example-bucket/cam-1/frame002.png",
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              "sensor_pose": {
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                "position": {
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                  "x": 0.01,
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                  "y": 0.1,
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                  "z": 0.1
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                },
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                "heading": {
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                  "w": 0.002,
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                  "x": 0.847,
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                  "y": -0.168,
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                  "z": 0.504
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                }
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              }
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            }
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          ]
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        }
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      ],
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      "sensor_meta" : [
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        {
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          "id": "lidar",
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          "name": "lidar",
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          "state": "editable",
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          "modality": "lidar",
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          "primary_view": true
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        },
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        {
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          "id": "cam-1",
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          "name": "cam-1",
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          "state": "editable",
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          "modality": "camera",
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          "camera_model": "brown_conrady",
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          "primary_view": false,
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          "intrinsics": {
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            "cx": 600,
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            "cy": 400,
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            "fx": 1200,
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            "fy": 800,
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            "k1": 0,
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            "k2": 0,
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            "k3": 0,
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            "k4": 0,
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            "p1": 0,
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            "p2": 0,
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            "skew": 0,
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            "scale_factor": 1
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          }
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        }
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      ]
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    }
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   },
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   "work_flow_id":"2aae1234-acac-1234-eeff-12a22a237bbc"
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}
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Payload definition
Key
Description
Type
data.sensor_data
Contains lists of frames and sensor metadata
Object
data.sensor_data.sensor_meta
Contains a list of all the sensors with each having metadata information like 
id : id of sensor
name : name of sensor 
modality : lidar / camera

If the sensor is a camera, you can add the camera intrinsic values as well as the camera_model. These values are used along with the sensor_pose to create projections between sensors.
​
camera_model:  one of brown_conrady or fisheye
If this key doesn't exist or is null, the tool will assume brown_conrady.
​
The intrinsics object contains the following keys:
cx: principal point x value
cy: principal point y value
fx: focal length in x-axis
fy: focal length in y-axis
k1, k2, k3, k4, k5, k6: Radial distortion coefficients
p1, p2: Tangential distortion coefficients
skew: camera skew coefficient
scale_factor: The factor by which the image has been downscaled (For example, scale_factor will be 2 if the original image is twice as large as the downscaled image)
​
If the camera_model is brown_conrady then the distortion coefficients should be one of the following combinations:
k1, k2, p1, p2
k1, k2, p1, p2, k3
k1, k2, p1, p2, k3, k4, k5, k6
If the camera_model is fisheye then the distortion coefficients should be the following combination:
k1, k2, k3, k4
The remaining coefficients can be ignored or be assigned a value of 0
​
OpenCV reference: Camera calibration and 3D reconstruction​
Object
data.sensor_data.frames
List of frames, each for a particular timestamp in the order of annotation. Each having frame_id, ego_pose and sensors
List
data.sensor_data.frames.[i].frame_id
Unique identifier of the particular frame
String
data.sensor_data.frames.[i].ego_pose
Contains the pose of a fixed point on the ego vehicle in the world frame of reference in the form of position (in (x, y, z)) and orientation (as quaternion (w, x, y, z))
​
In case the pose of the ego vehicle is available in the world frame of reference, The tool can allow annotators to mark objects as stationary and toggle APC (Aggregated point cloud) mode.

Usually, if a vehicle is equipped with an IMU or Odometry sensor, then it is possible to get the pose of the ego-vehicle in the world frame of reference.
Object
data.sensor_data.frames.[i].sensors
List of all the sensors associated with this particular frame with each having:
​
sensor_id : id of the sensor. This is a foreign key to the sensor id mentioned in the sensor_meta of the sequence data
​
data_url : A URL to the file containing the data captured from the sensor for this frame. In order to annotate lidar data, please share point clouds in ascii encoded PCD format.

sensor_pose : This key specifies the pose of respective sensors in a common frame of reference. 

If the ego_pose is available in the world frame of reference, then you should specify the sensor_pose of individual sensors in the same world frame of reference. In such cases, the pose might change in every frame, as the vehicle moves. 

If the ego_pose is not available, then all sensor_pose can be specified with respect to a fixed point on the vehicle. In such cases, the pose will not change between frames.
​
Please share point clouds in ascii encoded PCD format. 
If you are sharing the ego_pose and sensor_pose in the world frame of reference, then the points in the PCD file should also be in the world frame of reference
PCD format Specification: https://pcl.readthedocs.io/projects/tutorials/en/latest/pcd_file_format.html​
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# .PCD v0.7 - Point Cloud Data file format 
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VERSION 0.7
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FIELDS x y z
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SIZE 4 4 4
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TYPE F F F
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COUNT 1 1 1
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WIDTH 47286
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HEIGHT 1
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VIEWPOINT 0 0 0 1 0 0 0
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POINTS 47286
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DATA ascii
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5075.773 3756.887 107.923
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5076.011 3756.876 107.865
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5076.116 3756.826 107.844
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5076.860 3756.975 107.648
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5077.045 3756.954 107.605
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5077.237 3756.937 107.559
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5077.441 3756.924 107.511
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5077.599 3756.902 107.474
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5077.780 3756.885 107.432
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5077.955 3756.862 107.391
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...
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