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.