A Low Cost Ground Truth Detection System for RoboCup Using the Kinect

Khandelwal, Piyush; Stone, Peter

doi:10.1007/978-3-642-32060-6_44

Cited by 11 publications

(6 citation statements)

References 6 publications

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“…The work presented in this paper uses a different vision approach based on a depth sensor instead of an intensity sensor. This work presents several similarities with the work of Khandelwal et al [5] that uses a Kinect sensors as a low cost ground truth detection system. As 3D sensor, a Kinect was chosen given its low price (making it possible to use in an aggressive environment such as RoboCup), its ability to directly provide 3D depth information and its refresh rate of 30 fps similar to the RGB camera used in the onmi direction vision system of the robots [6].…”

Section: Introductionmentioning

confidence: 77%

Detection of Aerial Balls Using a Kinect Sensor

Dias

Silva

Castro

et al. 2015

RoboCup 2014: Robot World Cup XVIII

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Abstract. Detection of objects in the air is a difficult problem to tackle given the dynamics and speed of a flying object. The problem is even more difficult when considering a non-controlled environment where the predominance of a given color is not guaranteed, and/or when the vision system is located on a moving platform. As an example, most of the Middle Size League teams in RoboCup competition detect the objects in the environment using an omni directional camera that only detects the ball when in the ground, and losing any precise information of the ball position when in the air. In this paper we present a first approach towards the detection of a ball flying using a Kinect camera as sensor. The approach only uses 3D data and does not consider, at this time, any additional intensity information. The objective at this stage is to evaluate how useful is the use of 3D information in the Middle Size League context. A simple algorithm to detect a flying ball and evaluate its trajectory was implemented and preliminary results are presented.

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Section: Introductionmentioning

confidence: 77%

Detection of Aerial Balls Using a Kinect Sensor

Dias

Silva

Castro

et al. 2015

RoboCup 2014: Robot World Cup XVIII

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“…The position error is directly influenced by method of determining robot final position in the field. As it is pointed out in [9], determined position is highly affected by robots' orientation on the field. This is true for the position calculated by arithmetic mean of the robot point-cloud and the reason is that the position would be placed where the point-cloud has more density which is the region closer to the camera in its direction.…”

Section: Resultsmentioning

confidence: 99%

“…Due to the drawbacks of mentioned approaches and thanks to low cost depth sensors -such as Microsoft Kinect and Asus Xtion TM -[9] [10] have suggested using point-clouds for ground-truth detection system. The calibration process in [9] requires user to identify ground points and field's landmarks in order to calculate the camera transformation to the field reference. Moreover, color calibration must be performed to make the system able to tell the robots' team color.…”

Section: Related Workmentioning

confidence: 99%

Ground-truth localisation system for humanoid soccer robots using RGB-D camera

Nezhad

Ghiasvand

2017

IJCVR

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An essential step to develop an autonomous mobile robot behavior is establishment of a groundtruth detection system which can be used for both the localisation and behavior analysis. Depend on the requirements of the application there are some constraints for such a system like: diversity in types of providing information, accuracy of the data and ease of use -since the calibration is needed in both color classification and geometry position -and also the cost of the system, which all should be considered. In this paper, a low cost system is presented, using Asus Xtion depth camera, capable of providing ground-truth position information of the humanoid robots and the ball which is required to estimate the errors of a localisation system in RoboCup Standard Platform League (SPL) and Humanoid kid Size League (HSL) soccer robots. Furthermore the user input requirements are reduced to very few compared to other similar systems. Source code of the system is available online at: https://github.com/mrlspl/ As the result of literature review, three main factors could be named as properties of an appropriate ground-truth system: Total system cost  Ease of system calibration procedure  Diversity in types of provided information In this paper we present a low cost ground-truth localisation system which minimizes user's role in system calibration and provides most of the information from the soccer match including: robots, ball and referees positions in addition to team color identification.SPL and HSL game fields (9 6 meters [1] and 6 4 [2]) and their corresponding robots -namely NAO and DARwIn -have been chosen as the system's testbed.

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“…The use of such knowledge is especially interesting for places with artificial landmarks or previously defined colour-coded objects, such as the RoboCup competition [130]. There are several examples of the application of this specific knowledge in RoboCup (e.g., [87], [131]). …”

Section: Feature Extractionmentioning

confidence: 99%

“…In addition, the problem of actively searching for an object in a 3D environment is studied under the constraint of a maximum search time using a visually guided humanoid robot with 26 degrees of freedom [5]. Another study follows the standard pattern recognition approach based on four main steps [131]: (i) preprocessing to achieve colour constancy and stereo pair calibration; (ii) segmentation using depth-continuity information; (iii) feature extraction based on visual saliency; and (iv) classification using a neural network. The main novelty of the approach lies in the feature extraction step, where the authors propose novel features derived from a visual saliency mechanism.…”

Section: What Should a Vision System In Gmrs Be Used For?mentioning

confidence: 99%

A Taxonomy of Vision Systems for Ground Mobile Robots

Gómez

Fernández-Caballero

García-Varea

et al. 2014

International Journal of Advanced Robotic Systems

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This paper introduces a taxonomy of vision systems for ground mobile robots. In the last five years, a significant number of relevant papers have contributed to this subject. Firstly, a thorough review of the papers is proposed to discuss and classify both past and the most current approaches in the field. As a result, a global picture of the state of the art of the last five years is obtained. Moreover, the study of the articles is used to put forward a comprehensive taxonomy based on the most up-to-date research in ground mobile robotics. In this sense, the paper aims at being especially helpful to both budding and experienced researchers in the areas of vision systems and mobile ground robots. The taxonomy described is devised from a novel perspective, namely in order to respond to the main questions posed when designing robotic vision systems: why?, what for?, what with?, how?, and where? The answers are derived from the most relevant techniques described in the recent literature, leading in a natural way to a series of classifications that are discussed and contextualized. The article offers a global picture of the state of the art in the area and discovers some promising research lines.

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A Low Cost Ground Truth Detection System for RoboCup Using the Kinect

Cited by 11 publications

References 6 publications

Detection of Aerial Balls Using a Kinect Sensor

Detection of Aerial Balls Using a Kinect Sensor

Ground-truth localisation system for humanoid soccer robots using RGB-D camera

A Taxonomy of Vision Systems for Ground Mobile Robots

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