Abujawad Rafid Siddiqui MCS-2010-22, pp. 60. COM/School of Computing, 2010.
Context: Effective vision processing is an important study area for mobile robots which use vision to detect objects. The problem of detecting small sized coloured objects (e.g. Lego bricks) with no texture information can be solved using either colour or contours of the objects. The shape of such objects doesn‟t help much in detecting the objects due to the poor quality of the picture and small size of the object in the image. In such cases it is seen that the use of hybrid techniques can benefit the overall detection of objects, especially, combining keypoint based methods with the colour based techniques. Robotic motion also plays a vital role in the completion of autonomous tasks. Mobile robots have different configurations for locomotion. The most important system is differential steering because of its application in sensitive areas like military tanks and security robot platforms. The kinematic design of a robotic platform is usually based on the number of wheels and their movement. There can be several configurations of wheels designs, for example differential drives, car-like designs, omni-directional, and synchro drives. Differential drive systems use speed on individual channels to determine the combined speed and trajectory of the robot. Accurate movement of the robot is very important for correct completion of its activities. Objectives: A vision solution is developed that is capable of detecting small sized colour objects in the environment. This has also been compared with other shape detection techniques for performance evaluation. The effect of distance on detection is also investigated for the participating techniques. The precise motion of a four-wheel differential drive system is investigated. The target robot platform uses a differential drive steering system and the main focus of this study is accurate position and orientation control based upon sensor data. Methods: For object detection, a novel hybrid method „HistSURF‟ is proposed and is compared with other vision processing techniques. This method combines the results of colour histogram comparison and detection by the SURF algorithm. A solution for differential steering using a Gyro for the rotational speed measurement is compared with a solution using a speed model and control outputs without feedback (i.e. dead reckoning). Results: The results from the vision experiment rank the new proposed method highest among the other participating techniques. The distance experiment indicates that there is a direct and inverse relation between the distance and detected SURF features. It is also indicated by the results that distance affects the detection rate of the new proposed technique. In case of robot control, the differential drive solution using a speed model has less error rate than the one that uses a Gyro for angle measurement. It is also clear from the results that the greater the difference of speeds among the channels the less smooth is the angular movement. Conclusions: The results indicate that by combining a key-point based technique with colour segmentation, the false positive rate can be reduced and hence object recognition performance improves . It has also become clear that the improved accuracy of the proposed technique is limited to small distances and its performance decreases rapidly with increase in the distance to target objects. For robot control, the results indicate that a Gyro alone cannot improve the movement accuracy of the robotic system due to a variable drift exhibited by the Gyro while in rotation. However, a Gyro can be effective if used in combination with a magnetometer and some form of estimation mechanism like a Kalman filter. A Kalman filter can be used to correct the error in the Gyro by using the output from the magnetometer, resulting in a good estimate.