Aqilah Baseri Huddin

An automated 3D scanning algorithm using depth cameras for door detection

This paper presents an investigation on the characteristics of Microsoft Kinect depth camera for door detection in an indoor environment. Autonomous vehicles usually have to rely on images when navigating indoors due to network limitations of an indoor environment. Locating a door for exit and entryway is one of the problems that need to be tackled when navigating indoors. In this paper, images from a depth camera are captured and used as a tool for detecting doors. The continuously varied ratios and depth differences in the door images have been analysed. An algorithm for door detection was developed using MATLAB. Experiments using different heights and depths of the Kinect sensor have been performed to verify the efficacy of the algorithm for indoor autonomous flying robots like the quadcopter. The algorithm developed is best performed in a clear path of 3.5 meters. The accuracy of the measurement was influenced by the low resolution of the depth images.

Review on the effects of age, gender, and race demographics on automatic face recognition

The performance of face recognition algorithms is affected by external factors and internal subject characteristics. Identifying these aspects and understanding their behaviors on performance can aid in predicting the performance of algorithms and in designing suitable acquisition settings at prospective locations to enhance performance. Factors that affect the performance of face recognition systems, such as pose, illumination, expression, and image resolution, are recognized as face recognition problems. These are substantially studied, and many algorithms have been developed to tackle these problems. However, the influence of population demographics (i.e., race, age, and gender) on face recognition performance has not received considerable attention. Early findings that deal with demographic influence give conflicting results. The studies conducted in the last decade resolve some of the contentions. Nonetheless, some findings have not reached consensus. Existing reviews on the influence of covariates are either outdated or do not cover the influence of demographic covariates on the performance of face recognition algorithms. This paper gives an intensive and focused review that covers recent research on demographic covariates. The effects of age, gender, and race covariates on face recognition are summarized based on these findings, and suggestions on the future direction of the field are given to have a significant understanding of these effects individually and their interactions with one another.

Entryway detection algorithm using Kinect's depth camera for UAV application

Small unmanned aerial vehicles (UAVs) are gaining popularity in aiding search and rescue teams in the wake of a disaster. When searching through ruins such as a collapsed building or a building under fire, it is almost impossible for the first rescue team to navigate inside the ruins in search for survivors. Small UAVs such as the quadcopter which is equipped with autonomous capabilities has the potential to navigate through the unknown ruins. One of the basic building blocks for any autonomous vehicle is a fast-detection sensor for detection and avoidance of obstacles. Payload and cost should also be considered when choosing the right sensor. In this study, a feature extraction algorithm using Microsoft Kinect depth camera is presented for application on a quadcopter operating in an indoor environment. The main objective of this project is to develop an algorithm that could detect entryway openings, based on the inputs from a Microsoft Kinect camera that will be mounted on a quadcopter. The algorithm is tested in a T-junction corridor of an office building, with objects such as walls, doors, glass, corridors, and fire extinguisher boxes occupying the space. The algorithm successfully detects all objects by using the depth information of each pixel in relative to other pixels. The ratio of each depth area is calculated to differentiate the entryway from the rest of the objects. The analysis reveals that the accepted ratio for entryway detection is 0.701 with +−5% error while values not within this range are considered as obstacles.

Characterization of DC brushless motor for an efficient multicopter design

The Unmanned Aerial Vehicles (UAV) has recently gained popularity in numerous industries due to its unique capabilities. Multicopters are increasingly replacing conventional helicopters and fixed wing planes as the most commonly manufactured Micro Aerial Vehicles, due to their reasonable price. Unlike the long flight operations of fixed wing planes, multicopters can only be operated for short durations. The performance of multicopter depend on the characteristic of the motor that had being used. Adding further payloads to multicopters will deteriorate the performance in terms of stability and flight time. The objective of this paper is to study the characteristics of brushless direct current motor for the purpose of multicopter design. The experiments to determine the characteristics of brushless direct current motor are divided into two parts. The first part of the experiment is the measurement of lift force, power consumption, and current drain under various loads. The next part of the experiment is the measurement of the propeller RPM. All experiments were conducted using the same propeller, motor and Electronic Speed Controller (ESC) setup. The data collected from the experiments is used to generate five characteristics graphs, which consist of propeller rotation versus throttle, power versus propeller rotation, lift versus throttle, current versus throttle and finally power versus throttle. Besides that, the graph of flight time vs throttle input was estimated. All the data was used to build a reference table to estimate the minimum take-off throttle input and flight time for various multicopter configurations. In conclusion, the reference table and the graphs can be useful in aiding multicopter designers in designing an octocopter.