The document presents a study on stereo camera calibration for autonomous vehicles, highlighting the importance of the intelligent transportation system in Indonesia. It details the calibration process using two Logitech webcams, including steps to find chessboard corner coordinates and calculate intrinsic and extrinsic values. Results are evaluated using RMSE values from various chessboard patterns and shooting scenarios.

1. UNIVERSITAS
KRISNADWIPAYANA
Jakarta - Indonesia,
22 December
2022
The 2022 2nd
International Seminar on Machine Learning, Optimization, & Data Science
ismode.unkris.ac.id
Stereo Camera Calibration For
Autonomous Car Applications
Muhammad Abdillah Rahmat, Indrabayu, Andani Achmad, Ejah
Umraeni Salam, dan Muhammad Fadhil Bin Bahrunnida

2. Introduction
Problem
 The development of Indonesian
transportation sector is very high.
 Intelligent Transportation System
(ITS) is a vastly developing field in
the world of transportation.
Most object detection research still
uses one camera
 How to implement calibration in-
camera stereo using a webcam
Proposed Solution
 Stereo camera calibration
for object detection
implementation

3. Literature Study
The research conducted by Abdelmoghit Zarane
et al. with the title Distance measurement
system for autonomous vehicles using stereo
camera.
1
The research conducted by Yasir Dawood Salman
et al. with the title of Distance Measurement For
Self-Driving Cars Using Stereo Camera.
2

4. System Stereo Design
The study used 2 Logitech webcams; c920 stream pro and c922 stream pro for camera calibration.
Logitech Webcam c922 stream pro
Logitech Webcam c920 stream pro

5. Stereo camera system stages
Stereo
Calibration
Stereo
Rectification
Stereo Match Triangulation

6. The steps taken to calibrate the c920 stream pro and c922 stream pro webcams in
this study are :
• Sets the number of angles in and the size of the chessboard pattern as well as the distance between the
lens and the shooting distance.
• Find the coordinates of each corner of the chessboard
• Find the coordinator of each corner of the chessboard at sub-pixel points
• Finding intrinsic and extrinsic values through each coordinate

7. Sets the number of angles in and the size of the chessboard pattern
as well as the distance between the lens and the shooting distance.
• The number of angles in the chessboard pattern
• The unit size of each pattern
• The distance between the camera lens to be stereotyped
• The shooting distance between the stereo camera and the chessboard pattern
Chessboard patterns used
1

8. Find the coordinates of each corner of the chessboard
The next step is to find the coordinates of each corner point on the chessboard using the cv2.findChessboardCorners
function.
In using this function 3 supporting arguments are needed :
• The source of the image
• The number of inner corners in the image
• The argument of the operation flags.
2
Find the coordinator of each corner of the chessboard at sub-pixel
points
In the calibration process, accuracy and precision are the most decisive values. To obtain an accurate result, it is
necessary to obtain the location of the angle with an accuracy level of up to sub-pixels.
3

9. Finding intrinsic and extrinsic values through each coordinate
The final step in the calibration process is to pass the 3D point on the real-world coordinates and the 2D location
of each coordinate in all images to the cv2.calibrateCamera function.
4

10. Result
RMSE value with chessboard with a pattern unit size of 3 cm
RMSE value with chessboard with a pattern unit size of 11
cm
In building a stereo vision system, the RMSE value is output by the cv2 function. Calibrate stereo becomes an
evaluative foundation in the calibration process. In this study, two types of chessboard patterns were used with
several shooting scenarios for camera calibration needs. Table 1 and Table 2 show the RMSE values obtained from
each shooting scenario for calibration needs

11. Thank You.