3D Ranging and Tracking using an Improved Lensless Smart Sensor

Abraham, Lizy; Urru, Andrea; Wilk, Mariusz P.; Walsh, Michael; O’Flynn, Brendan

doi:10.1109/icinpro43533.2018.9096859

Cited by 2 publications

(5 citation statements)

References 7 publications

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“…Optoelectronics have been widely and consistently used in robotics over the recent years, particularly in the research field of collaborative systems and are shown to increase the safety of human operators. In the future, the price drop of optoelectronic sensors and the release of more compact and easier to implement hybrid and data fusion solutions, as well as next-generation wearable lens-less cameras [ 83 , 84 , 85 ], will lead to fewer obstructions in jobsites and improve the practicality of camera-based approaches in other industry sectors.…”

Section: Discussionmentioning

confidence: 99%

Motion Capture Technology in Industrial Applications: A Systematic Review

Menolotto

Komaris

Tedesco

et al. 2020

Sensors

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169

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The rapid technological advancements of Industry 4.0 have opened up new vectors for novel industrial processes that require advanced sensing solutions for their realization. Motion capture (MoCap) sensors, such as visual cameras and inertial measurement units (IMUs), are frequently adopted in industrial settings to support solutions in robotics, additive manufacturing, teleworking and human safety. This review synthesizes and evaluates studies investigating the use of MoCap technologies in industry-related research. A search was performed in the Embase, Scopus, Web of Science and Google Scholar. Only studies in English, from 2015 onwards, on primary and secondary industrial applications were considered. The quality of the articles was appraised with the AXIS tool. Studies were categorized based on type of used sensors, beneficiary industry sector, and type of application. Study characteristics, key methods and findings were also summarized. In total, 1682 records were identified, and 59 were included in this review. Twenty-one and 38 studies were assessed as being prone to medium and low risks of bias, respectively. Camera-based sensors and IMUs were used in 40% and 70% of the studies, respectively. Construction (30.5%), robotics (15.3%) and automotive (10.2%) were the most researched industry sectors, whilst health and safety (64.4%) and the improvement of industrial processes or products (17%) were the most targeted applications. Inertial sensors were the first choice for industrial MoCap applications. Camera-based MoCap systems performed better in robotic applications, but camera obstructions caused by workers and machinery was the most challenging issue. Advancements in machine learning algorithms have been shown to increase the capabilities of MoCap systems in applications such as activity and fatigue detection as well as tool condition monitoring and object recognition.

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Section: Discussionmentioning

confidence: 99%

Motion Capture Technology in Industrial Applications: A Systematic Review

Menolotto

Komaris

Tedesco

et al. 2020

Sensors

Self Cite

169

View full text Add to dashboard Cite

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“…In order to make the system usable in a wide range of environmental conditions, infrared LEDs have been used in the tracking system, as they are impervious to white light sources in standard daylight conditions, when using the LSS in combination with an infrared filter. The method to range and track a single light source is well described in previous work [ 41 , 42 ]. The work described in this paper is focused on developing a method for tracking multiple light points simultaneously, and thereby estimating hand location and orientation; thus, enabling smart tracking of a hand.…”

Section: Methodsmentioning

confidence: 99%

“…The calibration is performed only once to save the relative distances of LEDs, and use them as a reference in the next phase of tracking. The raw images are captured and reconstructed according to the method explained in [ 41 ]. In short, the spiral-shaped diffractive grating of the LSS creates equally spaced dual spirals for a single point source of the light, as shown in Figure 6 a.…”

Section: Methodsmentioning

confidence: 99%

“…The 2D coordinates in the image domain, relative to the two sensors together with the assigned labels, are processed according to the point tracking algorithm [ 41 , 42 ], using the reference that the PSF created. Thus, a matrix of relative distances of the palm coordinates is computed, as given in Table 1 using Equation (1).…”

Section: Methodsmentioning

confidence: 99%

“…However, instead of getting the image as such, the sensors will only provide unclear blob-like structures [ 40 ] that are rich in information. A proper reconstruction algorithm is developed using the blob data and the knowledge of grating to reconstruct the image without losing accuracy, which is well explained in previous works [ 41 , 42 ]. The sensing technology, as well as the optical, mathematical, and computational foundations of the LSS, are discussed in detail in the works of Stroke [ 43 ] and Gill [ 44 ].…”

Section: Rambus Lensless Smart Sensormentioning

confidence: 99%

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Hand Tracking and Gesture Recognition Using Lensless Smart Sensors

Abraham

Urru

Normani

et al. 2018

Sensors

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The Lensless Smart Sensor (LSS) developed by Rambus, Inc. is a low-power, low-cost visual sensing technology that captures information-rich optical data in a tiny form factor using a novel approach to optical sensing. The spiral gratings of LSS diffractive grating, coupled with sophisticated computational algorithms, allow point tracking down to millimeter-level accuracy. This work is focused on developing novel algorithms for the detection of multiple points and thereby enabling hand tracking and gesture recognition using the LSS. The algorithms are formulated based on geometrical and mathematical constraints around the placement of infrared light-emitting diodes (LEDs) on the hand. The developed techniques dynamically adapt the recognition and orientation of the hand and associated gestures. A detailed accuracy analysis for both hand tracking and gesture classification as a function of LED positions is conducted to validate the performance of the system. Our results indicate that the technology is a promising approach, as the current state-of-the-art focuses on human motion tracking that requires highly complex and expensive systems. A wearable, low-power, low-cost system could make a significant impact in this field, as it does not require complex hardware or additional sensors on the tracked segments.

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3D Ranging and Tracking using an Improved Lensless Smart Sensor

Cited by 2 publications

References 7 publications

Motion Capture Technology in Industrial Applications: A Systematic Review

Motion Capture Technology in Industrial Applications: A Systematic Review

Hand Tracking and Gesture Recognition Using Lensless Smart Sensors

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