2 research outputs found
Object Distance Measurement System Using Monocular Camera on Vehicle
To support autonomous vehicles that are currently often studied by various parties, the authors propose to make a system of predicting the distance of objects using monocular cameras on vehicles. Distance prediction uses four methods and the input parameter was obtained from images processed with MobileNets SSD. Calculations using linear regression are the simplest calculations among the four methods but have an error of 1% with a standard deviation of 1.65 meters. While using the first method, the average error value is 9% with a standard deviation of 0.43 meters. By using the second calculation, the average error resulted in 6% with a standard deviation of 0.35 meters. The experimental method had an average error of 1% with a standard deviation of 0.26 meters, so the experimental method was used
Object distance measurement using a single camera for robotic applications
Visual servoing is defined as controlling robots by extracting data obtained from
the vision system, such as the distance of an object with respect to a reference frame, or the length and width of the object. There are three image-based object distance
measurement techniques: i) using two cameras, i.e., stereovision; ii) using a single
camera, i.e., monovision; and iii) time-of-flight camera.
The stereovision method uses two cameras to find the object’s depth and is highly
accurate. However, it is costly compared to the monovision technique due to the higher
computational burden and the cost of two cameras (rather than one) and related
accessories. In addition, in stereovision, a larger number of images of the object need to
be processed in real-time, and by increasing the distance of the object from cameras, the
measurement accuracy decreases. In the time-of-flight distance measurement technique,
distance information is obtained by measuring the total time for the light to transmit to
and reflect from the object. The shortcoming of this technique is that it is difficult to
separate the incoming signal, since it depends on many parameters such as the intensity
of the reflected light, the intensity of the background light, and the dynamic range of the
sensor. However, for applications such as rescue robot or object manipulation by a robot
in a home and office environment, the high accuracy distance measurement provided by
stereovision is not required. Instead, the monovision approach is attractive for some
applications due to: i) lower cost and lower computational burden; and ii) lower
complexity due to the use of only one camera.
Using a single camera for distance measurement, object detection and feature
extraction (i.e., finding the length and width of an object) is not yet well researched and there are very few published works on the topic in the literature. Therefore, using this
technique for real-world robotics applications requires more research and improvements.
This thesis mainly focuses on the development of object distance measurement
and feature extraction algorithms using a single fixed camera and a single camera with
variable pitch angle based on image processing techniques. As a result, two different
improved and modified object distance measurement algorithms were proposed for cases
where a camera is fixed at a given angle in the vertical plane and when it is rotating in a
vertical plane. In the proposed algorithms, as a first step, the object distance and
dimension such as length and width were obtained using existing image processing
techniques. Since the results were not accurate due to lens distortion, noise, variable light
intensity and other uncertainties such as deviation of the position of the object from the
optical axes of camera, in the second step, the distance and dimension of the object
obtained from existing techniques were modified in the X- and Y-directions and for the
orientation of the object about the Z-axis in the object plane by using experimental data
and identification techniques such as the least square method.
Extensive experimental results confirmed that the accuracy increased for
measured distance from 9.4 mm to 2.95 mm, for length from 11.6 mm to 2.2 mm, and for
width from 18.6 mm to 10.8 mm. In addition, the proposed algorithm is significantly
improved with proposed corrections compared to existing methods. Furthermore, the
improved distance measurement method is computationally efficient and can be used for
real-time robotic application tasks such as pick and place and object manipulation in a
home or office environment.Master's Thesi