Differences between Kinect and structured lighting sensor in robot navigation

Konar

2016 International Joint Conference on Neural Networks (IJCNN)

et al. 2016

Abstract-Patients with prosthesis defects find it is very difficult to perform day-to-day basic tasks which involve employment of their limbs. This motivates us to develop a system where an artificial limb is employed to mimic the arm gestures of the patients for assisting them. Towards developing this system, we have taken the help from the electroencephalography (EEG) signals acquired from the brain of the patients to build a bypass network (BPN) to direct the artificial limb. Since difficulties are already present in the arm movements of the patients (here subjects), thus only gestures of those subjects are not sufficient to build the proposed system. This research finds tremendous applications in rehabilitative aid for the disable persons. To concretize our goal we have developed an experimental setup, where the target subject (for training phase healthy subjects are taken into account) is asked to catch a ball while his/her brain (occipital, parietal and motor cortex) signals using EEG acquisition device and body gestures using Kinect sensor are simultaneously acquired. These data are mapped using four cascade-correlation learning architecture (CCLA) to train artificial limb (we have used Jaco robot arm) to move accordingly. Utilizing the mapping results obtained from these four CCLAs, a BPN is developed. When a rehabilitative patient is unable to catch the ball, then in that scenario, the artificial limb is helpful for assisting the patient to catch the ball with a high accuracy of 85.65%. The proposed system can be implemented not only for ball catching experiment but also in several applications where an artificial limb needs to perform a locomotive task based on EEG and body gesture.

Section: Technical Overviewmentioning

confidence: 99%

Section: ) Training Phase 1 (Tp1)mentioning

confidence: 99%

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EEG based gesture mimicking by an artificial limb using cascade-correlation learning architecture

Konar

2016 International Joint Conference on Neural Networks (IJCNN)

et al. 2016

Proceeding of the 11th World Congress on Intelligent Control and Automation

“…[4][5][6]. The Kinect has been widely used in the study of mobile robot in recent years for its advantages of simple structure, low cost and easy using [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

A vision measurement algorithm based on four-lines structured light for robots environment perception

Song

Wang

Xiao

et al. 2014

Environment perception system is the base of autonomous navigation and control task of a mobile robot, therefore, the strength of the environment perception will impact the intelligence of a robot directly. A vision measurement algorithm based on four-lines structured light for robots environment perception is proposed in this paper. The algorithm is implemented on an embedded image processing board which contains an FPGA and a DSP. Four line lasers project four laser lines on the object to be measured, and a camera captures the image that contains light stripes which have been modulated by the surface of the object. Then the 3D coordinates of points on the laser lines can be obtained through the distortion of light stripes in the image. Each light stripe is limited in a certain region of the image so as to not overlap with each other. Therefore, the installation angle of every laser has been limited in a certain range. A sectional line fitting algorithm is proposed to improve the accuracy of centers of light stripes, thus improving the accuracy of the whole measurement system. A number of measurement results validate the effectiveness of the proposed method. Furthermore, we combine the 3D information obtained by the proposed method with the 2D edge feature of the image to construct a rough depth map of the environment. Index Terms -Vision measurement, Line structured light, Robot environment sensing.

Journal of Forensic Sciences

“…It is receiving a lot of attention for the low‐cost, reliability, and speed of the measurements provided . Kinect is currently one of the primary 3D measurement devices in indoor robotics and mapping, 3D scene reconstruction and object recognition . The use of these sensors in applications beyond entertainment is of great interest, being forensic science one of the applications fields with main possibilities.…”

mentioning

confidence: 99%

Application of Kinect Gaming Sensor in Forensic Science

González-Jorge

Zancajo

González‐Aguilera

et al. 2014

Kinect sensor appears as a low-cost option for 3D modeling. This manuscript describes a methodology to test the applicability of Kinect to crime scenes. The methodology includes the comparison versus well-established scanners (Faro and Trimble). The parameters used for the comparison are the quality in the fitting of primitives, a qualitative evaluation of facial data, the data quality for different ranges, and the accuracy in the measurement of different lengths. The results show that the Kinect noise level increases with range, from 5 mm at 1.5 m range to 15 mm at 3 m range. It is considered that for detail measurements the sensor must be placed close to the target. A general measurement of a sample crime scene was analyzed. Errors in length measurements are between 2% and 10% for 3 m range. The measurement range must be limited to c. 3 m.