Proceedings of 2nd International Symposium for Color Science and Art 2020

Accuracy Verification for Self-Localization Depending onColor Calibration Skill
Hidekazu Suzuki, Kaori Watanabe, Yuehang Ma
Tokyo Polytechnic Univ., 1583 Iiyama, Atsugi, Kanagawa, 243-0297, JapanABSTRACT
We developed a self-localization technique using anomnidirectional camera for an autonomous soccer-playing
robot. Positional information is important for the robot’s
strategic behavior and cooperation. We developed aself-localization method, which uses the white lines on thesoccer field to generate a search space model, and afitness function in a genetic algorithm (GA) to identify therobot’s position on the pitch. We conducted an experimentto verify the accuracy of this method and confirmed thedifference in positional accuracy by using team members
who adjust the parameters for color detection.
1. INTRODUCTION
RoboCup’s main research goal is to study how
multi-robot and multi-agent systems engage andcooperate with each other in dynamic situations, throughthe game of soccer [1]. On the RoboCup soccer field,
robots use self-localization to estimate their own position,
as well as the positions of the goals and other robots. They
then use this information to decide on a strategy. Therobot uses image information, environment information,and field information. In this paper, we describe a real-time
self-localization method that applies a genetic algorithm(GA) for the RoboCup middle size league (MSL), which
has the largest field (12 x 18 m).

Fig. 1. Mechanicallayout of our robot

Fig. 2. Process of making
search model

3.1 Search model
Figure 2 shows the process of making the search modelwith our method. We obtain the white detection image by
converting the color space from RGB to HSV and YUV,
and we generate the field information by orthogonalizing
the white line information, as shown in Fig. 2(b).

2. Hardware structure
We developed a robot with recent MSL technology: ahigh-torque-driving module, ball-control module, electrical
kicking module using a solenoid, and USB 3.0 camera
system.
The omnidirectional vision system (Fig. 1) of our robot
consisted of a FLIR Flea3 camera, Vstone varifocal lens,and Vstone hyperboloidal mirror. An image captured bythis vision system is shown in Fig. 2(a), and the image sizeand frame rate are 512 x 512 pixels and 30 fps,
respectively.

3.2. Model-based matching
Our self-localization method uses model-basedmatching to compare geometric information from the whitelines on the MSL field and the search model to generate
the search space. Let us denote the set of pixels thatcompose the search model shown in Fig. 3, as . The
positionand orientationof the search modelin the image are represented as. Then themovement ofin the matching area is expressed. By denoting the pixel value of the field imageas

3. Self-localization method
The robots use the white lines on the MSL field forself-localization. We developed a self-localization methodthat uses model-based matching to generate a search
space based on the white line information from the soccer
pitch. It then uses the fitness function to express therobot’s correct position as the maximum value of thefunction. The self-localization method uses a GA [2] tooptimize the fitness function.

,corresponding to the area of the moving model as, the evaluation functionof the moving
model is given as follows.

37Proceedings of 2nd International Symposium for Color Science and Art 2020
The fitness function
obtains the maximum value
when the position of the search model corresponds to thesame position as the robot on the MSL field. The problemof detecting the robot’s position and orientation isconverted to a search problem of such thatismaximized [3].
3.3 Genetic algorithm
In our self-localization method, we use a GA to find themaximum value of the fitness function. GAs are atype of artificial intelligence and are parallel
search-and-optimization processes that mimic naturalselection and evolution. We use an elitist model topreserve the best individual in the population in every
generation, genetic coding by gray code, roulette selectionand one-point crossover. The parameters of the GA
process are determined by previous experiments.

Fig. 3. Model matching

3.4 Verification
We performed an experiment to verify the effectivenessof the proposed self-localization method. Figure 4(a)shows the error between the correct position and thedetected position in a quarter of the MSL field at an intervalof one meter. The severity of the error is indicated by thebrightness of the gray scale. The average error was 13.5cm, so the self-localization method is accurate enough forplaying soccer.
Figure 4(b) shows another experimental result in thesame situation for another team member who does nothave enough color calibration skill for self-localization, i.e.,a beginner team member. The average error was 38.7 cm,
and the maximum error was 866 cm. These results meanthat the robot sometimes does not recognize the position
correctly.
Figure 5 shows the search models of each experiment.
The search model of Fig. 5(a) generated by a specialist
who is experienced in color calibration for this task canexpress the white line shape of the pitch well. However,the search model of Fig. 5(b) generated by a beginner
expresses only part of its shape because the parameterset for color detection is not suitable in this experimentalenvironment. In this case, the parameters, especially theparameters for detecting green, were incorrect.

(a) Specialist(b) BeginnerFig. 4. Self-localization error(a) Specialist(b) BeginnerFig. 5. Search model

4. Conclusion
In this paper, we described a self-localization methodthat generates a search space using model-basedmatching combined with white line information from theRoboCup MSL soccer field. It calculates the position of therobot by using a genetic algorithm to optimize the fitness
function. We verified the effectiveness of theself-localization method and confirmed that it is accurateenough for playing soccer.
Moreover, we also verified the difference in accuracy ofself-localization between specialist and beginner. We
confirmed that its accuracy is completely different
depending on color calibration skill.

5. References[1] RoboCup Federation official website,
https://www.robocup.org/[2] D.E. Goldberg, “Genetic Algorithms in Search,
Optimization and Machine Learning",
Addison-Wesley, 1989.
[3] K. Watanabe, Y. Ma, T. Yoshida, H. Suzuki, "Real-Time
Self Localization for Autonomous Robot of RoboCupMSL", Proceedings of The 2020 International
Conference on Artificial Life and Robotics(ICAROB2020), OS22-1, pp. 94-97, 202038