Introduction
Proposed approach
Results
Conclusions

ATOM Calibration Framework
Atomic Transformations Optimization Method

Miguel Oliveira12 , Daniela Rato12 , Manuel Gomes12 ,Daniel Coelho12 , Eurico Pedrosa1 , Nuno Lau1 , Vı́tor Santos12
1

Institute of Electronics and Informatics Engineering of Aveiro
University of Aveiro, Portugal
2

Department of Mechanical Engineering
University of Aveiro, Portugal

RosCON 2022

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Introduction
Proposed approach
Results
Conclusions

Table of Contents
1

Introduction
Motivation2

Proposed approach
Proposed formulation
Calibration framework

3

Results
Robotic systems and datasets
Evaluations
Comparison with other ROS calibration frameworks

4

Conclusions
Final remarks

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Introduction
Proposed approach
Results
Conclusions

Motivation

Table of Contents
1

Introduction
Motivation2

Proposed approach
Proposed formulation
Calibration framework

3

Results
Robotic systems and datasets
Evaluations
Comparison with other ROS calibration frameworks

4

Conclusions
Final remarks

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Introduction
Proposed approach
Results
Conclusions

Motivation

The robots of the futureWith the development of more
complex robotic and intelligent
systems, many now contain several
sensors, often of multiple
modalities
Examples of such systems:
Autonomous cars
AGVs, UAVs
Service robots
Camera networks

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Introduction
Proposed approach
Results
Conclusions

Motivation

Sensor calibration

Multi-sensor or multi-modal
perceptual systems operate under
the assumption that accurate
geometrical transformations between
the sensors that collect the data areknown

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Introduction
Proposed approach
Results
Conclusions

Motivation

Sensor calibration

Multi-sensor or multi-modal
perceptual systems operate under
the assumption that accurate
geometrical transformations between
the sensors that collect the data are
known
Sensor calibration
The process by which thesetransformations are estimated

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Introduction
Proposed approach
Results
Conclusions

Motivation

Sensor calibration variants

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Introduction
Proposed approach
Results
Conclusions

Motivation

Sensor calibration variants

Sensor to sensor
Stereo cameras, etc

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Introduction
Proposed approach
Results
Conclusions

Motivation

Sensor calibration variants

Sensor to sensor
Stereo cameras, etc

Sensor in motion
SFM, SLAM, MVS

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Introduction
Proposed approach
Results
Conclusions

Motivation

Sensor calibration variants

Sensor to sensor
Stereo cameras, etc

Sensor in motion
SFM, SLAM, MVS

Sensor to frame
Hand eye calibration

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Introduction
Proposed approach
Results
Conclusions

Motivation

Sensor calibration variants

Sensor to sensor
Stereo cameras, etc

Sensor in motion
SFM, SLAM, MVS

Sensor to frame
Hand eye calibration

Additional complexity
Multi-sensor (n ≥ 3)
Multi-modal

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Introduction
Proposed approach
Results
Conclusions

Proposed formulation
Calibration framework

Table of Contents
1

Introduction
Motivation2

Proposed approach
Proposed formulation
Calibration framework

3

Results
Robotic systems and datasets
Evaluations
Comparison with other ROS calibration frameworks

4

Conclusions
Final remarks

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Introduction
Proposed approach
Results
Conclusions

Proposed formulation
Calibration framework

Proposed formulation

solution = argmin

XX X 
e s Tpc , d[c,s] , {λs }{T̂ },{λ̂} s∈S c∈C d∈D

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Introduction
Proposed approach
Results
Conclusions

Proposed formulation
Calibration framework

Proposed formulation

solution = argmin

XX X 
e s Tpc , d[c,s] , {λs }{T̂ },{λ̂} s∈S c∈C d∈D

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Introduction
Proposed approach
Results
Conclusions

Proposed formulation
Calibration framework

Proposed formulation

solution = argmin

XX X 
e s Tpc , d[c,s] , {λs }{T̂ },{λ̂} s∈S c∈C d∈D

M. Oliveira et al. “ATOM: A general calibration framework for multi-modal, multi-sensor systems”. In: Expert Systems with Applications(2022), p. 118000. issn: 0957-4174. doi: https://doi.org/10.1016/j.eswa.2022.118000

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Introduction
Proposed approach
Results
Conclusions

Proposed formulation
Calibration framework

Calibration framework

Is that all you need to carry out a successful calibration?

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Introduction
Proposed approach
Results
Conclusions

Proposed formulation
Calibration framework

Calibration framework

Is that all you need to carry out a successful calibration?
Additional procedures / steps are required
How to collect data from the sensors?
How to define the topology of the transformations?
How to define the initial pose of the sensors?
How to visualize the procedures?
How to evaluate the calibration?

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Introduction
Proposed approach
Results
Conclusions

Proposed formulation
Calibration framework

Framework architecture - Overview

External modules
Robotics System
Configuration
Data logging

ATOM modules
Calibration configuration
Initial positioning of sensors
Data collection and labelling
Dataset playback
Calibration

ROS integrated tools for each
module10 / 43

Introduction
Proposed approach
Results
Conclusions

Proposed formulation
Calibration framework

MMTBOT
The Multi-Modal Test roBOT
is a conceptual robot designed
to test advanced calibration
methodologies
Contains the following sensors
A LiDAR mounted on a
tripodA camera mounted on thesame tripod
A camera mounted on theend effector of a manipulator

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Introduction
Proposed approach
Results
Conclusions

Proposed formulation
Calibration framework

MMTBOT
The Multi-Modal Test roBOT
is a conceptual robot designed
to test advanced calibration
methodologies
Contains the following sensors
A LiDAR mounted on a
tripodA camera mounted on thesame tripod
A camera mounted on theend effector of a manipulator

Because MMTBOT is simultaneously a multi-sensor, multi-modal, sensor
to sensor, as well as a sensor to frame calibration problem, there is no
solution in the literature which is able to conduct the complete,simultaeneous, calibration of this system
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Introduction
Proposed approach
Results
Conclusions

Proposed formulation
Calibration framework

Framework architecture - Robotic System Configuration

A description of the robot in
URDF
ROS standard
Defines kinematics, dynamics
and metadata
Readable and writable byboth humans and machines

Not just for ATOM
Robot visualization
Collision detection
Dynamic path planning

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Introduction
Proposed approach
Results
Conclusions

Proposed formulation
Calibration framework

Framework architecture - Data Logging

A recording of the traffic of
ROS message
Sensor data
Transformations data

The ability to process the data
offline13 / 43

Introduction
Proposed approach
Results
Conclusions

Proposed formulation
Calibration framework

Framework architecture - Data Logging

https://youtu.be/WCb1E6wEh30
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Introduction
Proposed approach
Results
Conclusions

Proposed formulation
Calibration framework

Framework architecture - Data Logging

https://youtu.be/MLgbZU9-byg
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Introduction
Proposed approach
Results
Conclusions

Proposed formulation
Calibration framework

Framework architecture - Calibration Configuration

Definition of calibration
parameters
Sensors to calibrate
Transformations to estimate
Pattern details

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Introduction
Proposed approach
Results
Conclusions

Proposed formulation
Calibration framework

Framework architecture - Calibration Configuration

https://youtu.be/QVfMB9DYego
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Introduction
Proposed approach
Results
Conclusions

Proposed formulation
Calibration framework

Framework architecture - Calibration Configuration

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Introduction
Proposed approach
Results
Conclusions

Proposed formulation
Calibration framework

Framework architecture - Initial positioning of sensors

Transformations from URDF
are often inaccurate
Initial values of parameters are
determinant for optimization
Interactive tool allows eachsensor to be repositioned online
Visual feedback
Plausible first guesses

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Introduction
Proposed approach
Results
Conclusions

Proposed formulation
Calibration framework

Framework architecture - Initial positioning of sensors

https://youtu.be/oJLKTqUtZvQ?t=111
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Introduction
Proposed approach
Results
Conclusions

Proposed formulation
Calibration framework

Framework architecture - Data collection and labelling

Goal is to create a set ofcollections - an ATOM dataset
User observes playback of data
and decides when to store
collections
Labelling provides useful realtime visual feedback

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Introduction
Proposed approach
Results
Conclusions

Proposed formulation
Calibration framework

Framework architecture - Data collection and labellinghttps://youtu.be/eII_ptyMq5E?t=130
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Introduction
Proposed approach
Results
Conclusions

Proposed formulation
Calibration framework

Framework architecture - Dataset playback

Data labelling can be generated
with errors
Leads to inaccurate calibration
Interactive tool allows manual
labelling of data
Available for LiDAR and depth
sensors

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Introduction
Proposed approach
Results
Conclusions

Proposed formulation
Calibration framework

Framework architecture - Dataset playback

https://youtu.be/kmEe3ewXmbU
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Introduction
Proposed approach
Results
Conclusions

Proposed formulation
Calibration framework

Framework architecture - Dataset playback

https://youtu.be/xkiwP4ZDz-s

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Introduction
Proposed approach
Results
Conclusions

Proposed formulation
Calibration framework

Framework architecture - Calibration

Provide visual feedback during
the optimization procedure
Visualization ofCollections and sensor data
Ground truth sensor poses
Current solution

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Introduction
Proposed approach
Results
Conclusions

Proposed formulation
Calibration framework

Framework architecture - Calibration

https://youtu.be/4B3X_NsX89M?t=351

27 / 43

Introduction
Proposed approach
Results
Conclusions

Robotic systems and datasets
Evaluations
Comparison with other ROS calibration frameworks

Table of Contents
1

Introduction
Motivation2

Proposed approach
Proposed formulation
Calibration framework

3

Results
Robotic systems and datasets
Evaluations
Comparison with other ROS calibration frameworks

4

Conclusions
Final remarks

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Introduction
Proposed approach
Results
Conclusions

Robotic systems and datasets
Evaluations
Comparison with other ROS calibration frameworks

Robotic Systems

We present the calibration of five distinct robotic systems
MMTBOT, simulated robotic system
AtlasCar2, autonomous car
AgrobV16, AGV for agriculture
Iris UR10e, experimental system for hand-eye calibration problems
LARCC, collaborative cell

These systems cover all of the calibration problems
The number of sensors and modalities differ from system to system

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Introduction
Proposed approach
Results
Conclusions

Robotic systems and datasets
Evaluations
Comparison with other ROS calibration frameworks

Robotic Systems

AtlasCar2

AgRob V16

Iris UR10e

LARCC

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Introduction
Proposed approach
Results
Conclusions

Robotic systems and datasets
Evaluations
Comparison with other ROS calibration frameworks

Datasets

SystemDescription

Sensors

DatasetDetails

MMTBOT(a)Simulation with robotic arm
All calibration problems

RGB cameras (2x)3D LiDAR

mmtbot-1
mmtbot-2

29 collections, 9 partial
47 collections, 25 incomplete, 16 partial

AtlasCar2(b)Autonomous vehicle
Sensor to sensor

RGB cameras (3x)2D LiDARs (2x)atlascar-1
atlascar-2

39 collections, 32 incomplete, 0 partial
21 collections, 18 incomplete, 0 partial

AgRob V16(c)AGV for agriculture
Sensor to Sensor

Stereo camera
3D LiDAR

agrob-1
agrob-2
agrob-3

42 collections, 5 incomplete, 38 partial
56 collections, 24 partial
15 collections, 8 partial

Iris UR10e(d)Hand-eye system
Sensor in motion, sensor to frame

RGB cameras (x2)iris-1iris-234 collections, all partial
26 collections, all partial

LARCC

Collaborative Cell
Sensor to sensor

RGB cameras (3x)3D LiDAR (3x)Depth camera

larcc-1
larcc-2

23 collection, 18 incomplete, 25 partial
29 collections, 23 incomplete, 61 partial(a) https://github.com/miguelriemoliveira/mmtbot(b) https://github.com/lardemua/atlascar2(c) https://github.com/aaguiar96/agrob(d) https://github.com/iris- ua/iris_ur10e

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Introduction
Proposed approach
Results
Conclusions

Robotic systems and datasets
Evaluations
Comparison with other ROS calibration frameworks

RGB to RGB camera evaluation
Three error metrics
mean rotation Rmean translation t
reprojection rms

t
(m)

(a)

(a)

rms
(pix)
(a)

0.001

0.001

0.531

MethodSensor pair

OpenCV
ATOM

world camera tohand camera

OpenCV
ATOM

world camera tohand camera

(a)

(a)

0.001

0.001

0.305

OpenCVATOM

left camera toright camera

0.018
0.027

0.013
0.032

1.157
1.198

OpenCV
ATOM

right camera tocenter camera

0.244
0.096

0.081
0.074

3.336
2.375

OpenCVATOM

center camera toleft camera

0.078
0.090

0.490
0.045

3.000
3.283

OpenCV
ATOM

world camera tohand camera

(b)

(b)

(b)

0.008

0.003

0.974

agrob-2

OpenCV
ATOM

world camera tohand camera

0.010
0.008

0.006
0.005

0.863
0.974

world camera tohand camera

(c)

(c)

(c)

ur10e-1

OpenCV
ATOM

0.015

0.002

1.093

world camera tohand camera

(c)

(c)

(c)

ur10e-2

OpenCV
ATOM

0.009

0.001

0.843

larcc-1

OpenCVATOM

pairwise combinations ofcamera 1 up to camera 4

-

-

0.627
0.563

larcc-2

OpenCVATOM

pairwise combinations ofcamera 1 up to camera 4

-

-

1.828
1.245

mmtbot-2

atlascar-1

OpenCV stereo calibration
All corners detected
Cameras cannot move

R
(rad)

Datasetmmtbot-1

agrob-1(a)(a) OpenCV cannot be used because the hand camera is not static(b) OpenCV cannot be used because the dataset contains partial detections(c) OpenCV cannot be used because of both (a) and (b)32 / 43

Introduction
Proposed approach
Results
Conclusions

Robotic systems and datasets
Evaluations
Comparison with other ROS calibration frameworks

RGB to RGB camera evaluation
Three error metrics
mean rotation Rmean translation t
reprojection rms

ATOM vs OpenCV
Similar accuracies, whilecalibrating complete systems

t
(m)

(a)

(a)

rms
(pix)
(a)

0.001

0.001

0.531

MethodSensor pair

OpenCV
ATOM

world camera tohand camera

OpenCV
ATOM

world camera tohand camera

(a)

(a)

0.001

0.001

0.305

OpenCVATOM

left camera toright camera

0.018
0.027

0.013
0.032

1.157
1.198

OpenCV
ATOM

right camera tocenter camera

0.244
0.096

0.081
0.074

3.336
2.375

OpenCVATOM

center camera toleft camera

0.078
0.090

0.490
0.045

3.000
3.283

OpenCV
ATOM

world camera tohand camera

(b)

(b)

(b)

0.008

0.003

0.974

agrob-2

OpenCV
ATOM

world camera tohand camera

0.010
0.008

0.006
0.005

0.863
0.974

world camera tohand camera

(c)

(c)

(c)

ur10e-1

OpenCV
ATOM

0.015

0.002

1.093

world camera tohand camera

(c)

(c)

(c)

ur10e-2

OpenCV
ATOM

0.009

0.001

0.843

larcc-1

OpenCVATOM

pairwise combinations ofcamera 1 up to camera 4

-

-

0.627
0.563

larcc-2

OpenCVATOM

pairwise combinations ofcamera 1 up to camera 4

-

-

1.828
1.245

mmtbot-2

atlascar-1

OpenCV stereo calibration
All corners detected
Cameras cannot move

R
(rad)

Datasetmmtbot-1

agrob-1(a)(a) OpenCV cannot be used because the hand camera is not static(b) OpenCV cannot be used because the dataset contains partial detections(c) OpenCV cannot be used because of both (a) and (b)32 / 43

Introduction
Proposed approach
Results
Conclusions

Robotic systems and datasets
Evaluations
Comparison with other ROS calibration frameworks

RGB to RGB camera evaluation
Three error metrics
mean rotation Rmean translation t
reprojection rms

ATOM vs OpenCV
Similar accuracies, whilecalibrating complete systems
Accurately calibrates datasets
OpenCV cannot tackle

t
(m)

(a)

(a)

rms
(pix)
(a)

0.001

0.001

0.531

MethodSensor pair

OpenCV
ATOM

world camera tohand camera

OpenCV
ATOM

world camera tohand camera

(a)

(a)

0.001

0.001

0.305

OpenCVATOM

left camera toright camera

0.018
0.027

0.013
0.032

1.157
1.198

OpenCV
ATOM

right camera tocenter camera

0.244
0.096

0.081
0.074

3.336
2.375

OpenCVATOM

center camera toleft camera

0.078
0.090

0.490
0.045

3.000
3.283

OpenCV
ATOM

world camera tohand camera

(b)

(b)

(b)

0.008

0.003

0.974

agrob-2

OpenCV
ATOM

world camera tohand camera

0.010
0.008

0.006
0.005

0.863
0.974

world camera tohand camera

(c)

(c)

(c)

ur10e-1

OpenCV
ATOM

0.015

0.002

1.093

world camera tohand camera

(c)

(c)

(c)

ur10e-2

OpenCV
ATOM

0.009

0.001

0.843

larcc-1

OpenCVATOM

pairwise combinations ofcamera 1 up to camera 4

-

-

0.627
0.563

larcc-2

OpenCVATOM

pairwise combinations ofcamera 1 up to camera 4

-

-

1.828
1.245

mmtbot-2

atlascar-1

OpenCV stereo calibration
All corners detected
Cameras cannot move

R
(rad)

Datasetmmtbot-1

agrob-1(a)(a) OpenCV cannot be used because the hand camera is not static(b) OpenCV cannot be used because the dataset contains partial detections(c) OpenCV cannot be used because of both (a) and (b)32 / 43

Introduction
Proposed approach
Results
Conclusions

Robotic systems and datasets
Evaluations
Comparison with other ROS calibration frameworks

LiDAR to RGB evaluation

Errors are higher w.r.t. RGB to
RGB evaluations

DatasetMethodSensor pair

rms
(pix)

mmtbot-1

ATOM

3D LiDAR to hand camera
3D LiDAR to world camera

4.466
3.108

mmtbot-2

ATOM

3D LiDAR to hand camera
3D LiDAR to world camera

4.439
6.284

ATOM pairwise

3D LiDAR to right camera
3D LiDAR to left camera

3.869
4.101

ATOM

3D LiDAR to right camera
3D LiDAR to left camera

3.811
3.942

ATOM pairwise

3D LiDAR to right camera
3D LiDAR to left camera

6.715
6.432

ATOM

3D LiDAR to right camera
3D LiDAR to left camera

6.537
6.765

larcc-1

ATOM

pairwise combinations oflidar 1 to lidar 3 andcamera 1 to camera 4

2.582

larcc-2

ATOM

pairwise combinations oflidar 1 to lidar 3 andcamera 1 to camera 4

3.098

agrob-1

agrob-2

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Introduction
Proposed approach
Results
Conclusions

Robotic systems and datasets
Evaluations
Comparison with other ROS calibration frameworks

LiDAR to RGB evaluation

Errors are higher w.r.t. RGB to
RGB evaluations
Full system calibrations show
no significant loss of accuracy

DatasetMethodSensor pair

rms
(pix)

mmtbot-1

ATOM

3D LiDAR to hand camera
3D LiDAR to world camera

4.466
3.108

mmtbot-2

ATOM

3D LiDAR to hand camera
3D LiDAR to world camera

4.439
6.284

ATOM pairwise

3D LiDAR to right camera
3D LiDAR to left camera

3.869
4.101

ATOM

3D LiDAR to right camera
3D LiDAR to left camera

3.811
3.942

ATOM pairwise

3D LiDAR to right camera
3D LiDAR to left camera

6.715
6.432

ATOM

3D LiDAR to right camera
3D LiDAR to left camera

6.537
6.765

larcc-1

ATOM

pairwise combinations oflidar 1 to lidar 3 andcamera 1 to camera 4

2.582

larcc-2

ATOM

pairwise combinations oflidar 1 to lidar 3 andcamera 1 to camera 4

3.098

agrob-1

agrob-2

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Introduction
Proposed approach
Results
Conclusions

Robotic systems and datasets
Evaluations
Comparison with other ROS calibration frameworks

LiDAR to RGB evaluation

Errors are higher w.r.t. RGB to
RGB evaluations
Full system calibrations show
no significant loss of accuracy
Advantage over sequential
pairwise systems
A framework that is able tocalibrate the entire systemsimultaneously with similar
accuracy is very useful

DatasetMethodSensor pair

rms
(pix)

mmtbot-1

ATOM

3D LiDAR to hand camera
3D LiDAR to world camera

4.466
3.108

mmtbot-2

ATOM

3D LiDAR to hand camera
3D LiDAR to world camera

4.439
6.284

ATOM pairwise

3D LiDAR to right camera
3D LiDAR to left camera

3.869
4.101

ATOM

3D LiDAR to right camera
3D LiDAR to left camera

3.811
3.942

ATOM pairwise

3D LiDAR to right camera
3D LiDAR to left camera

6.715
6.432

ATOM

3D LiDAR to right camera
3D LiDAR to left camera

6.537
6.765

larcc-1

ATOM

pairwise combinations oflidar 1 to lidar 3 andcamera 1 to camera 4

2.582

larcc-2

ATOM

pairwise combinations oflidar 1 to lidar 3 andcamera 1 to camera 4

3.098

agrob-1

agrob-2

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Introduction
Proposed approach
Results
Conclusions

Robotic systems and datasets
Evaluations
Comparison with other ROS calibration frameworks

LiDAR to LiDAR evaluation

Errors measured in mm

DatasetMethodSensor pair

rms
(mm)

larcc-1

ATOM
ICP

pairwise combinations oflidar 1 to lidar 3

32.97433.931

larcc-2

ATOM
ICP

pairwise combinations oflidar 1 to lidar 3

53.56153.31434 / 43

Introduction
Proposed approach
Results
Conclusions

Robotic systems and datasets
Evaluations
Comparison with other ROS calibration frameworks

LiDAR to LiDAR evaluation

Errors measured in mm

DatasetMethodSensor pair

rms
(mm)

Comparison with an ICP
algorithm shows no loss inaccuracylarcc-1

ATOM
ICP

pairwise combinations oflidar 1 to lidar 3

32.97433.931

larcc-2

ATOM
ICP

pairwise combinations oflidar 1 to lidar 3

53.56153.31434 / 43

Introduction
Proposed approach
Results
Conclusions

Robotic systems and datasets
Evaluations
Comparison with other ROS calibration frameworks

Depth to LiDAR evaluation

Errors measured in pixels

DatasetMethodSensor pair

rms
(pix)

larcc-1

ATOM
ICP

pairwise combinations ofdepth camera 1 and lidar 1 to lidar 3

1.306
2.751

larcc-2

ATOM
ICP

pairwise combinations ofdepth camera 1 and lidar 1 to lidar 3

1.819
5.206

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Introduction
Proposed approach
Results
Conclusions

Robotic systems and datasets
Evaluations
Comparison with other ROS calibration frameworks

Depth to LiDAR evaluation

Errors measured in pixels
Comparison with an ICP
algorithm shows no loss inaccuracy

DatasetMethodSensor pair

rms
(pix)

larcc-1

ATOM
ICP

pairwise combinations ofdepth camera 1 and lidar 1 to lidar 3

1.306
2.751

larcc-2

ATOM
ICP

pairwise combinations ofdepth camera 1 and lidar 1 to lidar 3

1.819
5.206

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Introduction
Proposed approach
Results
Conclusions

Robotic systems and datasets
Evaluations
Comparison with other ROS calibration frameworks

Comparison with other ROS calibration frameworks

ATOM vs robot calibration and industrial calibration
ATOM creates a calibration framework automatically, generating
needed launch files, scripts, etc...
ATOM calibrates LiDAR and depth sensors
ATOM does not need to previously know the transformations
between the moving pattern and the sensor
ATOM collects data offline
ATOM does not need to configure data collection
ATOM supports (and recommends) ChArUcO

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Introduction
Proposed approach
Results
Conclusions

Final remarks

Table of Contents
1

Introduction
Motivation2

Proposed approach
Proposed formulation
Calibration framework

3

Results
Robotic systems and datasets
Evaluations
Comparison with other ROS calibration frameworks

4

Conclusions
Final remarks

37 / 43

Introduction
Proposed approach
Results
Conclusions

Final remarks

Conclusions

ATOM is a calibration framework
ATOM is fully integrated into ROS
ATOM is easy to use
Tackles sensor to sensor, sensor in motion and sensor to frame
Tackles any number of sensors of multiple modalities
Achieves similar levels of accuracy when compared to other pairwise
methods

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Introduction
Proposed approach
Results
Conclusions

Final remarks

Publications
M. Oliveira et al. “ATOM: A general calibration framework for multi-modal,
multi-sensor systems”. In: Expert Systems with Applications (2022), p. 118000.
issn: 0957-4174. doi: https://doi.org/10.1016/j.eswa.2022.118000
D. Rato et al. “A sensor-to-pattern calibration framework for multi-modal
industrial collaborative cells”. In: Journal of Manufacturing Systems 64 (2022),
pp. 497–507. doi: 10.1016/j.jmsy.2022.07.006
A. Aguiar et al. “A Camera to LiDAR calibration approach through the
Optimization of Atomic Transformations”. In: Expert Systems with Applications(2021). IF 5.423 (2020), p. 114894. issn: 0957-4174. doi:
10.1016/j.eswa.2021.114894
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Introduction
Proposed approach
Results
Conclusions

Final remarks

ATOM’s repository

https://github.com/lardemua/atom
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Introduction
Proposed approach
Results
Conclusions

Final remarks

ATOM repository’s stars

https://github.com/lardemua/atom
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Introduction
Proposed approach
Results
Conclusions

Final remarks

ATOM’s documentation

https://github.com/lardemua/atom
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Introduction
Proposed approach
Results
Conclusions

Final remarks

Please take a look and use it!

https://github.com/lardemua/atom
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