{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/41752"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/41752","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"Design of a low-cost optical motion capture system using a multi-camera configuration and an asynchronous extended kalman filter","abstract":"Motion capture technology, originating from the entertainment industry, has expanded its applications to various fields including robotics, medical and healthcare, the automotive industry, and virtual and augmented reality. Despite its versatility, the high cost of off-the-shelf commercial motion capture systems makes this technology inaccessible for many smaller institutions and businesses. This dissertation presents the design and development of a low-cost optical motion capture system using a multi-camera setup and a novel algo-rithm that embeds the camera model within an extended Kalman filter (EKF) for precise tracking of a robot's pose. The goal of this dissertation is to reduce the cost of an optical motion capture system by a factor of 7, targeting a total system cost of approximately $900. In comparison, off-the-shelf commercial optical motion capture systems currently cost over $6,600. The methodology includes initial simulation of the system in MATLAB, which is enhanced by real-world experimentation using affordable cameras programmed to track predefined features on a rigid-body robot. These cameras use image processing techniques to transmit pixel coordinate locations to a local base station, where the EKF algorithm processes the data to estimate the robot's pose. Experimental testing results demonstrates the system's ability to achieve a position and orientation accuracy of less than 1 cm and 2◦, respectively, within a 2 × 2 × 2 m capture space, at a cost of $883,34, which is significantly lower when compared to off-the-shelf commercial systems. The development revealed significant challenges in balancing cost and performance, pri-marily due to the limitations of low-cost cameras. The accuracy of motion capture is heavily dependent on camera specifications such as resolution and refresh rate. As cam-era performance improves, costs rise dramatically. The implications of this research are broad, offering a foundation for future explorations into cost-effective motion capture so-lutions. The current work is completely opensource and offered as an invitation to share and collaborate with other institutes of interest.","abstract_html":"Motion capture technology, originating from the entertainment industry, has expanded its applications to various fields including robotics, medical and healthcare, the automotive industry, and virtual and augmented reality. Despite its versatility, the high cost of off-the-shelf commercial motion capture systems makes this technology inaccessible for many smaller institutions and businesses. This dissertation presents the design and development of a low-cost optical motion capture system using a multi-camera setup and a novel algo-rithm that embeds the camera model within an extended Kalman filter (EKF) for precise tracking of a robot's pose. The goal of this dissertation is to reduce the cost of an optical motion capture system by a factor of 7, targeting a total system cost of approximately $900. In comparison, off-the-shelf commercial optical motion capture systems currently cost over $6,600. The methodology includes initial simulation of the system in MATLAB, which is enhanced by real-world experimentation using affordable cameras programmed to track predefined features on a rigid-body robot. These cameras use image processing techniques to transmit pixel coordinate locations to a local base station, where the EKF algorithm processes the data to estimate the robot's pose. Experimental testing results demonstrates the system's ability to achieve a position and orientation accuracy of less than 1 cm and 2◦, respectively, within a 2 × 2 × 2 m capture space, at a cost of $883,34, which is significantly lower when compared to off-the-shelf commercial systems. The development revealed significant challenges in balancing cost and performance, pri-marily due to the limitations of low-cost cameras. The accuracy of motion capture is heavily dependent on camera specifications such as resolution and refresh rate. As cam-era performance improves, costs rise dramatically. The implications of this research are broad, offering a foundation for future explorations into cost-effective motion capture so-lutions. 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Despite its versatility, the high cost of off-the-shelf commercial motion capture systems makes this technology inaccessible for many smaller institutions and businesses. This dissertation presents the design and development of a low-cost optical motion capture system using a multi-camera setup and a novel algo-rithm that embeds the camera model within an extended Kalman filter (EKF) for precise tracking of a robot's pose. The goal of this dissertation is to reduce the cost of an optical motion capture system by a factor of 7, targeting a total system cost of approximately $900. In comparison, off-the-shelf commercial optical motion capture systems currently cost over $6,600. The methodology includes initial simulation of the system in MATLAB, which is enhanced by real-world experimentation using affordable cameras programmed to track predefined features on a rigid-body robot. These cameras use image processing techniques to transmit pixel coordinate locations to a local base station, where the EKF algorithm processes the data to estimate the robot's pose. Experimental testing results demonstrates the system's ability to achieve a position and orientation accuracy of less than 1 cm and 2◦, respectively, within a 2 × 2 × 2 m capture space, at a cost of $883,34, which is significantly lower when compared to off-the-shelf commercial systems. The development revealed significant challenges in balancing cost and performance, pri-marily due to the limitations of low-cost cameras. The accuracy of motion capture is heavily dependent on camera specifications such as resolution and refresh rate. As cam-era performance improves, costs rise dramatically. The implications of this research are broad, offering a foundation for future explorations into cost-effective motion capture so-lutions. The current work is completely opensource and offered as an invitation to share and collaborate with other institutes of interest."]},{"key":"dc:title","label":"Title","values":["Design of a low-cost optical motion capture system using a multi-camera configuration and an asynchronous extended kalman filter"]}]}],"canonical_facts":{"dc:contributor.advisor":["Pretorius, Arnold","Hepworth, James"],"dc:creator":["Meyer, Zakariya"],"dc:date.accessioned":["2025-09-10T11:55:52Z"],"dc:date.available":["2025-09-10T11:55:52Z"],"dc:date.issued":["2025"],"dc:description.abstract":["Motion capture technology, originating from the entertainment industry, has expanded its applications to various fields including robotics, medical and healthcare, the automotive industry, and virtual and augmented reality. Despite its versatility, the high cost of off-the-shelf commercial motion capture systems makes this technology inaccessible for many smaller institutions and businesses. This dissertation presents the design and development of a low-cost optical motion capture system using a multi-camera setup and a novel algo-rithm that embeds the camera model within an extended Kalman filter (EKF) for precise tracking of a robot's pose. The goal of this dissertation is to reduce the cost of an optical motion capture system by a factor of 7, targeting a total system cost of approximately $900. In comparison, off-the-shelf commercial optical motion capture systems currently cost over $6,600. The methodology includes initial simulation of the system in MATLAB, which is enhanced by real-world experimentation using affordable cameras programmed to track predefined features on a rigid-body robot. These cameras use image processing techniques to transmit pixel coordinate locations to a local base station, where the EKF algorithm processes the data to estimate the robot's pose. Experimental testing results demonstrates the system's ability to achieve a position and orientation accuracy of less than 1 cm and 2◦, respectively, within a 2 × 2 × 2 m capture space, at a cost of $883,34, which is significantly lower when compared to off-the-shelf commercial systems. The development revealed significant challenges in balancing cost and performance, pri-marily due to the limitations of low-cost cameras. The accuracy of motion capture is heavily dependent on camera specifications such as resolution and refresh rate. As cam-era performance improves, costs rise dramatically. The implications of this research are broad, offering a foundation for future explorations into cost-effective motion capture so-lutions. 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