Luhur Bayuaji

Study on 3D scene reconstruction in robot navigation using stereo vision

In this paper, a 3D scene reconstruction by using stereo vision is presented. Stereo camera parameters from the camera calibration process and disparity map are two important parameters to obtain an accurate result for the 3D reconstruction. 3D reconstruction process generates the coordinates of world points (point cloud). From the information of the point cloud, the interested object is segmented out. Additionally, the noises left in the image is eliminated. The experimental result obtained shows that only the background and the interested object from the overall scene appeared in the processed image. The center of gravity is also determined as the reference value for the robot navigation. An estimation error model is also introduced in this study to increase the accuracy of the distance measurement by using the developed stereo vision system from the mobile robot. This experiment provides a foundation for navigation and object tracking for a mobile robot.

Performance of various speckle reduction filters on Synthetic Aperture Radar image

Synthetic Aperture Radar (SAR) image with its advantages, becoming popular than the optical image. However, the speckle in causes difficulties in the interpretation and analysis during image processing. Thus, before the SAR images are used, speckle noise reduction is necessary. The ideal speckle filter has the main goal of reducing speckle noise without losing the information, content, and preserve the edges and features. Various noise filters have been designed for different purposes and different capacities. In this study, we discuss four filters, namely Lee, Frost, Median and Mean filter. We are analyzing quality parameter and comparing statistic performance of Lee, Frost, Mean and Median filters for SAR sample data. The results show MSE, PSNR, SNR, and AD value that generate by Frost filter performs better than the other filter. And from visual interpretation of the de-speckle image that filtered with Frost filter, show sharpen edge and preserved texture.

Comparison of fuzzy filters on Synthetic Aperture Radar image

The Synthetic Aperture Radar (SAR) image with its advantages is becoming more popular than the optical image in earth observation in using the remote-sensing techniques. However, the speckle noise that occurs in the SAR image causes difficulties in image interpretation. Thus, speckle noise reduction needs prepossessing procedure prior to the use of the SAR images. This study is done by proposed fuzzy filters that utilize SAR data. From the comparison, the combination of Frost-Triangular Moving Average (TMAV) has the best performance in the ability to reduce speckle noise than other filters. This filter improved the Frost filter performance for speckle noise reduction parameters measurement, shows that 13.41% for Equivalent Number of Looks (ENL) and 6.07% for Speckle Index (SI). While Frost-Asymmetric Triangular Moving Average (ATMAV) has a relatively good performance for preserved texture. This filter improved the texture parameters such as Standard Deviation improved 4.33% and improved Variance for 8.46%. However, for the Mean parameters, Frost-Triangular Median Center (TMED) combination has the best performance compared to other filters, which improved the mean value for 7.10%. The comparative study it has been verified that the fuzzy approach has the robustness in the reduction of speckle noise and preserving the texture when applied in SAR image.

Motion Tracker Based Wheeled Mobile Robot System Identification and Controller Design

This project deals with the mathematical modelling and controller design for autonomous Wheeled Mobile Robot (WMR) by using motion tracking system. The mobile robot vehicle has two driving wheels and the angular speed of the two wheels is the controlled variable. Three reflected markers are attached on a robot to form a 3D rigid body. Motion tracker will track the 3D rigid body in terms of x, y position and orientation θ. Mathematical modelling which is a set of Multi Input Single Output model is done by using System Identification Toolbox in Matlab. Three different controller namely Proportional (P), Proportional Differential (PD) and Proportional Integral Differential (PID) controller are designed for this WMR. The mathematical model obtained from the System Identification has about 95% accuracy. In controller performance, the result shows that P, PD and PID controller have no overshoot for the forward movement. However, the percent overshoot of P, PD and PID controller when the robot is turning on side direction are around 51%, 63% and 48%, respectively. Additionally, the steady state error for all controllers is 0%.

Waypoint Navigation of Quad-rotor MAV Using Fuzzy-PID Control

Quad-rotor Micro Aerial Vehicle (MAV) is a multi-rotor MAV with four propellers which propel the MAV up to the air and move around. It has high maneuverability to move around, such as roll, pitch and yaw movements. However, line of sight and radio control effective range are the major limitation for the MAVs which significantly shorten the travel distance. Therefore, we proposed a waypoint navigation quad-rotor MAV based on Fuzzy-PID controller in this paper. User can set mission with multiple waypoint and the Fuzzy-PID controller will control MAV autonomously moving along the waypoint to the desired position without remotely controlled by radio control and guidance of pilot. The results show Fuzzy-PID controller is capable to control MAV to move to the desired position with high accuracy. As the conclusion, Fuzzy-PID controller is successfully designed for waypoint navigation in quad-rotor MAV. The result shows that the overshoot percentage (%OS) of the designed Fuzzy-PID controller for x position is 2.17% while y position is 0.93%. Additionally, the steady-state error for x position and y position are 0.54% and 0.56% respectively. Therefore, the performance of Fuzzy-PID controller is better than PID controller.

SKF-Based Image Template Matching for Distance Measurement by Using Stereo Vision

In this paper, a novel image template matching approach to tackle distance measurement problem has been proposed. There are many conventional algorithms to increase the accuracy of distance measurement as reported in the literature such as Semi-global algorithm to produce the disparity map. Meanwhile, in this paper, the reverse engineering technique had been implemented to get the correct depth value by applying the image template matching method as reference for the distance measurement. The traditional algorithm to solve image matching problem take a lot of memory and computational time. Therefore, image matching problem can be considered to optimization problem and can be solved precisely. The search of the image template has been performed exhaustively by using Simulated Kalman Filter (SKF) algorithm. The experiment is conducted with a set of images taken by using stereo vision system. Experimental results show the accuracy of the distance measurement by using stereo camera, after applying (1) the estimate error model, (2) SKF and (3) PSO algorithm are 89.95%, 96.09%, 95.29% and 58.51% respectively. The limitation of estimate error model that it can only be applied into the same setup of the experiment, environment, parameters of the camera and acquired images. Instead, the proposed algorithm which is SKF can be applied to original image and image under the vision problems like illumination and partially occluded. The SKF algorithm shows more robust, more efficient and more accurate to solve the distance measurement problem.

Fault detection and isolation for complex system

Fault Detection and Isolation (FDI) is a method to monitor, identify, and pinpoint the type and location of system fault in a complex multiple input multiple output (MIMO) non-linear system. A two wheel robot is used as a complex system in this study. The aim of the research is to construct and design a Fault Detection and Isolation algorithm. The proposed method for the fault identification is using hybrid technique that combines Kalman filter and Artificial Neural Network (ANN). The Kalman filter is able to recognize the data from the sensors of the system and indicate the fault of the system in the sensor reading. Error prediction is based on the fault magnitude and the time occurrence of fault. Additionally, Artificial Neural Network (ANN) is another algorithm used to determine the type of fault and isolate the fault in the system.

Land cover classification using Landsat data (study area: Pekan district, Pahang state, Malaysia)

In order to investigate the terrain changed in areas of urban, vegetation, bare land, and water type mapping in Pekan, Pahang of Malaysia using Landsat data. The data of a district of educational and research on years 2000 and 2013, were analyzed. First, will evaluating the quality of the image of Landsat data. There were no obvious errors noticeable on the geometric and radiometric. In order to estimate the accuracy of the map derived from classification, a ground truth map was prepared using strip sampling on map. Image classification was performed using original and synthetic bands for following four land types: urban, water, bare land, vegetation, and cloud types. Classification was performed using maximum likelihood (ML). The highest accuracy was obtained by using ML classifier. According to the results of this study, the spectral data of Landsat data have a large potential for land type mapping in Pekan area mapping.