Deepen AI - Enterprise
Deepen AI
  • Deepen AI Overview
  • FAQ
  • Saas & On Premise
  • Data Management
    • Data Management Overview
    • Creating/Uploading a dataset
    • Create a dataset profile
    • Auto Task Assignments
    • Task Life Cycle
    • Tasks Assignments
    • Creating a group
    • Adding a user
    • Embed labeled dataset
    • Export
    • Export labels
    • Import Labels
    • Import profile
    • Import profile via JSON
    • Access token for APIs
    • Data Streaming
    • Reports
    • Assessments
  • 2D/3D editors
    • Editor Content
    • AI sense (ML-assisted labeling)
    • Assisted 2d Segmentation
    • Scene Labeling
  • 2D Editor
    • 2D Editor Overview
    • 2D Bounding Boxes
    • 2D Polyline/Line
    • 2D Polygon
      • 2D Semantic/Instance Segmentation
        • 2D Segmentation (foreground/background)
    • 2D Points
    • 2D Semantic Painting
      • Segment Anything
      • Propagate Labels in Semantic Segementation
      • 2D Semantic Painting/Segmentation Output Format
    • 3D Bounding boxes on images
    • 2D ML-powered Visual Object Tracking
    • 2D Shortcut Keys
    • 2D Customer Review
  • 3D Editor
    • 3D Editor Overview
    • 3D Bounding Boxes — Single Frame/Individual Frame
    • 3D Bounding Boxes_Sequence
    • 3D Bounding Boxes Features
      • Label View
      • One-Click Bounding Box
      • Sequence Timeline
      • Show Ground Mesh
      • Secondary Views
      • Camera Views
      • Hide/UnHide Points in 3D Lidar
    • 3D Lines
    • 3D Polygons
    • 3D Semantic Segmentation/Painting
    • 3D Instance Segmentation/Painting
    • Fused Cloud
    • 3D Segmentation (Smart Brush)
    • 3D Segmentation (Polygon)
    • 3D Segmentation (Brush)
    • 3D Segmentation (Ground Polygon)
    • 3D Painting (Foreground/Background)
    • 3D Segmentation(3D Brush/Cube)
    • Label Set
    • 3D Shortcut Keys
    • 3D Customer Review
  • 3D input/output
    • JSON input format for uploading a dataset in a point cloud project.
    • How to convert ROS bag into JSON data for annotation
    • Data Output Format - 3D Semantic Segmentation
    • Data Output Format - 3D Instance Segmentation
  • Quality Assurance
    • Issue Creation
    • Automatic QA
  • Calibration
    • Calibration
    • Charuco Dictionary
    • Calibration FAQ
    • Data Collection for Camera intrinsic Calibration
    • Camera Intrinsic calibration
    • Data Collection for Lidar-Camera Calibration (Single Target)
    • Lidar-Camera Calibration (Single target)
    • Data Collection for Lidar-Camera Calibration (Targetless)
    • Lidar-Camera Calibration (Targetless)
    • Data Collection for Multi Target Lidar-Camera Calibration
    • Multi Target Lidar-Camera Calibration
    • Lidar-Camera Calibration(Old)
    • Vehicle-Camera Calibration
      • Data Collection for Vehicle-Camera Calibration
      • Vehicle Camera Targetless Calibration
      • Data collection for lane based targetless vehicle-camera calibration
      • Lane based Targetless Vehicle Camera Calibration
    • Data Collection for Rough Terrain Vehicle-Camera Calibration
    • Rough Terrain Vehicle-Camera Calibration
    • Calibration Toolbar options
    • Calibration Profile
    • Data Collection for Overlapping-Camera Calibration
    • Overlapping-Camera Calibration
    • Data collection guide for Overlapping Camera Calibration (Multiple-Targets)
    • Overlapping Camera Calibration (Multiple-Targets)
    • Data Collection for Vehicle-3D Lidar calibration
    • Data Collection for Vehicle-2D Lidar calibration
    • Vehicle Lidar (3D and 2D) Calibration
    • Data Collection for Vehicle Lidar Targetless Calibration
    • Data Collection for IMU Lidar Targetless Calibration
    • Vehicle Lidar Targetless Calibration
    • Data Collection for Non Overlapping Camera Calibration
    • Non-Overlapping-Camera Calibration
    • Multi Sensor Visualization
    • Data Collection for LiDAR-LiDAR Calibration
    • LiDAR-LiDAR Calibration
    • Data Collection for IMU Intrinsic calibration
    • IMU Intrinsic Calibration
    • Data Collection for Radar-Camera Calibration
    • Radar-Camera Calibration
    • Data Collection for IMU Vehicle calibration
    • Lidar-IMU Calibration
    • IMU Vehicle Calibration
    • Data Collection for vehicle radar calibration
    • Vehicle radar calibration
    • Calibration Optimiser
    • Calibration list page
    • Data collection for rough terrain vehicle-Lidar calibration
    • Rough terrain vehicle Lidar calibration
    • Surround view camera correction calibration
    • Data Collection for Surround view camera correction calibration
    • Data Collection for Lidar-Radar calibration
    • Lidar Radar Calibration
    • Vehicle Lidar Calibration
    • API Documentation
      • Targetless Overlapping Camera Calibration API
      • Target Overlapping Camera Calibration API
      • Lidar Camera Calibration API
      • LiDAR-LiDAR Calibration API
      • Vehicle Lidar Calibration API
      • Global Optimiser
      • Radar Camera Calibration API
      • Target Camera-Vehicle Calibration API
      • Targetless Camera-Vehicle Calibration API
      • Calibration groups
      • Delete Calibrations
      • Access token for APIs
    • Target Generator
  • API Reference
    • Introduction and Quickstart
    • Datasets
      • Create new dataset
      • Delete dataset
    • Issues
    • Tasks
    • Process uploaded data
    • Import 2D labels for a dataset
    • Import 3D labels for a dataset
    • Download labels
    • Labeling profiles
    • Paint labels
    • User groups
    • User / User Group Scopes
    • Download datasets
    • Label sets
    • Resources
    • 2D box pre-labeling model API
    • 3D box pre-labeling model API
    • Output JSON format
Powered by GitBook
On this page
  • Introduction
  • Folder Structure
  • config.json for checkerboard
  • config.json for charucoboard
  • Quickstart
  • API Reference
  • Get Extrinsic Parameters
Export as PDF
  1. Calibration

Vehicle Lidar Calibration

PreviousLidar Radar CalibrationNextAPI Documentation

Last updated 2 years ago

Introduction

The API requires the client to upload the PCD (pcap, csv, and bin are also supported), and configuration for vehicle lidar setup in a zip file (.zip extension) in the format defined below. The contents of the zip file are called a dataset.

  1. The client makes an Upload and calibrate API call, which uploads their files and runs the calibration algorithm on the lidar files for the given configuration.

  2. The calibration process is completed without errors if the response to the Upload and calibrate API call will contain datasetId and Status as Done.

  3. The client can call the Get Extrinsic Parameters API using the datasetId obtained from the Upload response and calibrate API. This API responds with the various extrinsic parameters, error stats, and the query's status.

Folder Structure

We require image and lidar frame pairs from the camera and lidar for a given calibration.

  1. Place the images captured from the camera in a folder.

  2. Place the Lidar data captured from the LiDAR in a folder.

  3. config.json contains configuration details of the calibration (intrinsic parameters, calibration name, etc.)

  1. The names of the folders and the images shown here are for demonstration purposes. Users should avoid using space in the folder and the image names.

  2. The name of the JSON file should be config.json (case sensitive)

config.json for checkerboard

{
    "calibration_name": "Lidar camera calibration",
    "calibration_type": "lidar_camera_calibration",
    "multi_target": false,
    "max_correspondence": 0.05,
    "deep_optimization": false,
    "lidar": {
        "name": "lidar"
    },
    "intrinsic_params": {
                "camera_name": "camera name",
                "fx": 4809.13303863791,
                "fy": 4804.6573641098275,
                "cx": 1994.0408528062305,
                "cy": 1441.0395643417517,
                "distortion_enabled": false,
                "lens_model": "pinhole",
                "k1": -0.03563526645635081,
                "k2": 0.2338404159941449,
                "k3": -1.3671429904044254,
                "k4": 0,
                "k5": 0,
                "k6": 0,
                "p1": -0.002478228973939787,
                "p2": -0.0026861612981927407
    },
    "targets": {
        "0": {
            "x": 7,
            "y": 8, 
            "type": "checkerboard",
            "square_size":0.12,
            "padding_right": 0.343,
            "padding_left": 0.22,
            "padding_top": 0.22,
            "padding_bottom": 0.22,
            "on_ground": false,
            "tilted": true
        }
    },

    "data": {
        "mappings": [
            [
                "camera/1.png",
                "lidar/1.pcd"
            ],
            [
                "camera/2.png",
                "lidar/2.pcd"
            ],
            [
                "camera/3.png",
                "lidar/3.pcd"
            ],
            [
                "camera/4.png",
                "lidar/4.pcd"
            ],
            [
                "camera/5.png",
                "lidar/5.pcd"
            ],
            [
                "camera/6.png",
                "lidar/6.pcd"
            ]  
        ]
    },
    "extrinsic_params_initial_estimates": {
        "roll": -91.22985012342338,
        "pitch": -1.8101400401363152,
        "yaw": -87.84825901836496,
        "px": 0.06356787067597357, 
        "py": -0.28854421270970754,
        "pz": -0.015338954542810408
    }
}

config.json for charucoboard

{
    "calibration_name": "Lidar camera calibration",
    "calibration_type": "lidar_camera_calibration",
    "multi_target": false,
    "max_correspondence": 0.05,
    "deep_optimization": false,
    "lidar": {
        "name": "lidar"
    },
    "intrinsic_params": {
                "camera_name": "camera name",
                "fx": 4809.13303863791,
                "fy": 4804.6573641098275,
                "cx": 1994.0408528062305,
                "cy": 1441.0395643417517,
                "distortion_enabled": false,
                "lens_model": "pinhole",
                "k1": -0.03563526645635081,
                "k2": 0.2338404159941449,
                "k3": -1.3671429904044254,
                "k4": 0,
                "k5": 0,
                "k6": 0,
                "p1": -0.002478228973939787,
                "p2": -0.0026861612981927407
    },  
    "targets":{
        "0": {
            "rows": 14,
            "columns": 14,
            "type": "charucoboard",
            "square_size":0.08708571428,
            "marker_size": 0.06966857142,
            "dictionary": "5X5",
            "padding_right": 0,
            "padding_left": 0,
            "padding_top": 0,
            "padding_bottom": 0,
            "on_ground": true,
            "tilted": false
        },
        "1": {
            "rows": 13,
            "columns": 13,
            "type": "charucoboard",
            "square_size":0.09378461538,
            "marker_size": 0.0750276923,
            "dictionary": "6X6",
            "padding_right": 0,
            "padding_left": 0,
            "padding_top": 0,
            "padding_bottom": 0,
            "on_ground": true,
            "tilted": false
        },
        "2": {
            "rows": 12,
            "columns": 12,
            "type": "charucoboard",
            "square_size":0.1016,
            "marker_size": 0.08128,
            "dictionary": "7X7",
            "padding_right": 0,
            "padding_left": 0,
            "padding_top": 0,
            "padding_bottom": 0,
            "on_ground": true,
            "tilted": false
        },
        "3": {
            "rows": 13,
            "columns": 13,
            "type": "charucoboard",
            "square_size":0.09378461538,
            "marker_size": 0.0750276923,
            "dictionary": "original",
            "padding_right": 0,
            "padding_left": 0,
            "padding_top": 0,
            "padding_bottom": 0,
            "on_ground": true,
            "tilted": false
        }
    },
    "data": {
        "mappings": [
            [
                "Images/charuco_2_1.jpg",
                "PCDs/charuco_2_1.pcd"
            ],
            [
                "Images/charuco_2_2.jpg",
                "PCDs/charuco_2_2.pcd"
            ],
            [
                "Images/charuco_2_3.jpg",
                "PCDs/charuco_2_3.pcd"
            ]
        ]
    },
    "extrinsic_params_initial_estimates": {
        "roll": -91.22985012342338,
        "pitch": -1.8101400401363152,
        "yaw": -87.84825901836496,
        "px": 0.06356787067597357, 
        "py": -0.28854421270970754,
        "pz": -0.015338954542810408
    }
}

Key

Value type

Description

calibration_name

string

Name of the calibration

calibration_type

string

Non-editable field.*Value should be lidar_camera_calibration

multi_target

boolean

true: if multiple targets are used false: if single target is used

max_correspondence

double

Accepted range is from 0 to 1

deep_optimisation

Boolean

Performs optimisation for the board edges. true: If tilted = true and deep optimisation is needed false: If deep optimisation is not required or the tilted = false

lidar_name

string

It is the name given by the client to the lidar. The client can modify it as willed.

camera_name

string

It is the name given by the client to the camera. The client can modify it as willed.

lens_model

string

Describes the type of lens used by the camera. Accepted values

  1. pinhole

  2. fisheye

fx

double

Focal length of the cameras in the X-axis. Value in pixels.

fy

double

Focal length of the camera in the Y-axis. Value in pixels.

cx

double

Optical centre of the camera in the X-axis. Value in pixels.

cy

double

Optical centre of the camera in the Y-axis. Value in pixels.

distortion_enabled

boolean

Makes use of distortion coefficients (k1, k2, k3, k4, p1, p2) for the calibration algorithm when set true. Distortion coefficients (k1, k2, k3, k4, p1, p2) are not required if it is false.

k1, k2, k3, k4, p1, p2

double

These are the values for distortion coefficients of the camera lens.Note:

  1. If the lens_model is pinhole we require k1, k2, k3, p1, and p2 values (no need of k4)

  2. If the lens_model is fisheye then we require the k1, k2, k3, and k4 values. (p1 and p2 are not needed)

  3. These parameters are not required if distortion_enabled is false.

targets

Object

It is a dictionary of dictionary with each dictionary having target properties

type

string

Describes the type of target used. Accepted values

  1. checkerboard

  2. charucoboard

x

integer

number of horizontaol corners in the checkerboard (this property is needed if the type = checkerboard)

y

integer

number of vertical corners in the checkerboar (this property is needed if the type = checkerboard)

rows

integer

number of horizontaol squares in the charucoboard (this property is needed if the type is charucoboard)

columns

integer

number of vertical squares in the charucoboard (this property is needed if the type is charcuboard)

square_size

double

Size of each square in meters

marker_size

double

The size of marker in a charucoboard in meters ( Normally it is 0.8 times of square size ) (this property is needed if the type is charucoboard)

dictionary

string

It is the string that defines the charuco dictionary of the target. We support

  1. 5X5

  2. 6X6

  3. 7X7

  4. original

This property is needed if the type is charucoboard

padding_right

double

padding to the right of the board

padding_left

double

padding to the left of the board

padding_top

double

padding to the top of the board

padding_bottom

double

padding to the bottom of the board

on_ground

Boolean

true: if the board is kept on ground

false: if the board is not on the ground

tilted

Boolean

true: if the board is tilted false: if the board is not tilted

data

Object

It stores the data related to mapping of the camera and the lidar files

mappings

List of lists

It is a list of lists, where each sub-list is a tuple containing names of the image and pcd paired together.

Note:

  1. The first element in the tuple should be the image path

  2. The second element in the tuple should be the lidar frame path from the lidar

  3. The client can name their image and lidar frame as they want, but they must have the same name in the mapping list and be present in the provided path

extrinsic_params_initial_estimates

Object with all values as double

The estimated extrinsic parameters which will be optimised during calibration process.

  1. roll

  2. pitch

  3. yaw

  4. px

  5. py

  6. pz

Quickstart

API Reference

Upload file and calibrate

This API sends a zip file to the server and runs the calibration algorithm. Returns datasetId, extrinsic parameters, and status to the user as the response.

URL

POST https://tools.calibrate.deepen.ai/api/v2/external/clients/{clientId}/calibration_dataset

Request

Path parameters

Parameter name
Parameter type
Description

clientId

string

ClientId obtained from Deepen AI

Body

Key
Value
Description

file

.zip file

Zip file containing config and images in a suitable format

Response

JSON file containing dataset_id and status of the calibration.

Response object:

{
    "Dataset ID": "XXXXXXXXXXXXXXXXX",
    "Extrinsic Parameters": {
        "roll": -90.47755237974575,
        "pitch": -0.38434110269976385,
        "yaw": -87.95967045393508,
        "px": 0.06958801619530329,
        "py": -0.28251980028661544,
        "pz": -0.010306058948604074
    },
    "Error Stats": {
        "translation_error": 0.04085960836364045,
        "rotation_error": 0.7512576778920595,
        "reprojection_error": 27.18615944133744
    },
    "Status": "done"
}

Key

Status

dataset_id

A unique value to identify the dataset. dataset_id can be used to retrieve the extrinsic parameters.

status

Current status of the dataset.

  1. ready: Files are uploaded, and the dataset is ready for Calibration.

  2. in_progress: The calibration process has started

  3. done: Calibration is done. Users can query for extrinsics.

Get Extrinsic Parameters

Returns the extrinsic parameters, error statistics, and the query's status.

URL

GET https://tools.calibrate.deepen.ai/api/v2/external/datasets/{datasetId}/extrinsic_parameters

Request

Path parameters

Parameter name
Parameter type
Description

datasetId

string

datasetId obtained from the response of Upload file and calibrate API.

Response

Returns a JSON dictionary containing datasetId, extrinsic parameters, error statistics, and query status.

Response Object:

{
    "Dataset ID": "XXXXXXXXXXXXXXXXX",
    "Extrinsic Parameters": {
        "roll": -90.47755237974575,
        "pitch": -0.38434110269976385,
        "yaw": -87.95967045393508,
        "px": 0.06958801619530329,
        "py": -0.28251980028661544,
        "pz": -0.010306058948604074
    },
    "Error Stats": {
        "translation_error": 0.04085960836364045,
        "rotation_error": 0.7512576778920595,
        "reprojection_error": 27.18615944133744
    }
}
Key
Description

dataset_id

A unique value to identify the dataset. dataset_id can be used to retrieve the extrinsic parameters.

extrinsic_parameters

roll, pitch, and yaw are given in degrees and px, py, and pz are given in meters.

error_stats

translation_error: Mean of difference between the centroid of points of checkerboard/charucoboard in the LiDAR and the projected corners in 3-D from an image

rotation_error: Mean of difference between the normals of the checkerboard/charucoboard in the point cloud and the projected corners in 3-D from an image reprojection_error: Mean of difference between the centroid of image corners and projected lidar checkerboard/charucoboard points on the image in 3-D

Before invoking the APIs, the client must obtain the clientId and auth token from Deepen AI. If you are a calibration admin, you can create different Access Tokens using the UI and use those instead. clientId is part of the path parameters in most API calls, and the auth token should be prefixed with “Bearer “ and passed to the ‘Authorization’ header in all API requests. How to get Access Tokens can be found on the following link:

Access token for APIs
2KB
config.json
config for checkboard
3KB
config.json
contents of the zip file
Contents of the Camera folder
Contents of the LiDAR folder