TY - JOUR
T1 - Simultaneous tracking and reconstruction of objects and its application in educational robotics laboratories
AU - Zhang, Mingshao
AU - Zhang, Zhou
AU - Chang, Yizhe
AU - Esche, Sven K.
N1 - Publisher Copyright:
© American Society for Engineering Education, 2015.
PY - 2015
Y1 - 2015
N2 - Many educational and industrial applications that involve robots require knowing the location information for the robots. This necessitates both the ability to localize the robots globally in the absence of any prior data as well as to track the robots' current positions once their initial locations are known. Various approaches have been used to solve these problems, such as encoders, inertial navigation, range sensing and vision-based techniques. Among those state-of-the-art robot localization methods, vision-based techniques are considered as some of the most effective approaches, and they can be enhanced significantly by obtaining additional supporting information from signal processing techniques and related algorithm developments. However, many challenges associated with the use of vision-based robot tracking systems in uncontrolled environments remain. For example, hardware components of visual odometry systems tend to be expensive and difficult to implement; choosing the most suitable algorithms and analysis methods is not straightforward and those algorithms are considered to be computationally expensive. In this paper, a visual odometry system implemented using a low-cost user-friendly 3-D scanner (the Microsoft Kinect) is presented. A traditional approach for robot tracking based on object recognition was applied, which includes building an object database, followed by extracting, describing and matching keypoints between the database and the scene. The advantages and disadvantages of using the Kinect in this approach were studied. Then, a technique for the simultaneous tracking and reconstruction (STAR) of objects was developed and tested. This technique was inspired by the simultaneous localization and mapping (SLAM) approach, and it was implemented using the Kinect and an iRobot Create platform. The prototype implementation shows that this STAR technique is feasible and suitable to be used in educational robotics laboratories. This technique also has multiple advantages compared to traditional educational laboratories, such as lower cost, more straightforward setup and less required preparation work by the laboratory instructor.
AB - Many educational and industrial applications that involve robots require knowing the location information for the robots. This necessitates both the ability to localize the robots globally in the absence of any prior data as well as to track the robots' current positions once their initial locations are known. Various approaches have been used to solve these problems, such as encoders, inertial navigation, range sensing and vision-based techniques. Among those state-of-the-art robot localization methods, vision-based techniques are considered as some of the most effective approaches, and they can be enhanced significantly by obtaining additional supporting information from signal processing techniques and related algorithm developments. However, many challenges associated with the use of vision-based robot tracking systems in uncontrolled environments remain. For example, hardware components of visual odometry systems tend to be expensive and difficult to implement; choosing the most suitable algorithms and analysis methods is not straightforward and those algorithms are considered to be computationally expensive. In this paper, a visual odometry system implemented using a low-cost user-friendly 3-D scanner (the Microsoft Kinect) is presented. A traditional approach for robot tracking based on object recognition was applied, which includes building an object database, followed by extracting, describing and matching keypoints between the database and the scene. The advantages and disadvantages of using the Kinect in this approach were studied. Then, a technique for the simultaneous tracking and reconstruction (STAR) of objects was developed and tested. This technique was inspired by the simultaneous localization and mapping (SLAM) approach, and it was implemented using the Kinect and an iRobot Create platform. The prototype implementation shows that this STAR technique is feasible and suitable to be used in educational robotics laboratories. This technique also has multiple advantages compared to traditional educational laboratories, such as lower cost, more straightforward setup and less required preparation work by the laboratory instructor.
UR - http://www.scopus.com/inward/record.url?scp=84941993663&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84941993663&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:84941993663
VL - 122nd ASEE Annual Conference and Exposition: Making Value for Society
JO - ASEE Annual Conference and Exposition, Conference Proceedings
JF - ASEE Annual Conference and Exposition, Conference Proceedings
IS - 122nd ASEE Annual Conference and Exposition: Making Value for...
T2 - 2015 122nd ASEE Annual Conference and Exposition
Y2 - 14 June 2015 through 17 June 2015
ER -