CHANGE FOR GOOD? AN ASSESSMENT OF THE DISCONNECT BETWEEN PERCEIVED BENEFITS BY ENGOS AND USER SATISFACTION OF SOLAR BOTTLE LIGHTS IN INFORMAL SETTLEMENTS OF DHAKA CITY, BANGLADESH

A major challenge for a developing country such as Bangladesh is to supply basic services to its most marginalized populations, which includes both rural and urban dwellers. The government struggles to provide basic necessities such as water and electricity. In marginalized urban communities in Bangladesh, in particular informal settlements, meeting basic needs is even direr. Most informal settlements are built to respond to a rapid immigration to urban centers, and are thought of as ‘temporary structures’, though many structures have been there for decades. In addition, as the settlements are often squatting on private land, access to formalized services such as electricity or water is largely absent. In some cases, electricity and water connections are brought in - but through informal and non-government sanctioned ways -- these hookups are deemed ‘illegal’ by the state. My research will focus on recent efforts to help ameliorate issues associated with lack of basic services in informal settlements in Bangladesh – in this case lack of light. When the government fails to meet the needs of the general population, different non-government organizations tend to step in to intervene. A new emphasis on solar bottle systems in informal urban settlement areas to help address some energy needs (specifically day-time lighting). One such example is the solar bottle light in Bangladesh, a project introduced by the organization ‘Change’. There has been mixed reactions on this technology among the users. This is where my research intervenes. I have used quantitative method to investigate user satisfactions for the solar bottle lights among the residents of the informal settlements to address the overarching question, is there a disconnect between the perceived benefits of the ENGO and the user satisfaction of the residents of the informal settlements of Dhaka City? This paper uses survey responses to investigate level of user satisfaction and the contributing factors.

POSE ESTIMATION AND 3D RECONSTRUCTION USING SENSOR FUSION

A camera maps 3-dimensional (3D) world space to a 2-dimensional (2D) image space. In the process it loses the depth information, i.e., the distance from the camera focal point to the imaged objects. It is impossible to recover this information from a single image. However, by using two or more images from different viewing angles this information can be recovered, which in turn can be used to obtain the pose (position and orientation) of the camera. Using this pose, a 3D reconstruction of imaged objects in the world can be computed. Numerous algorithms have been proposed and implemented to solve the above problem; these algorithms are commonly called Structure from Motion (SfM). State-of-the-art SfM techniques have been shown to give promising results. However, unlike a Global Positioning System (GPS) or an Inertial Measurement Unit (IMU) which directly give the position and orientation respectively, the camera system estimates it after implementing SfM as mentioned above. This makes the pose obtained from a camera highly sensitive to the images captured and other effects, such as low lighting conditions, poor focus or improper viewing angles. In some applications, for example, an Unmanned Aerial Vehicle (UAV) inspecting a bridge or a robot mapping an environment using Simultaneous Localization and Mapping (SLAM), it is often difficult to capture images with ideal conditions. This report examines the use of SfM methods in such applications and the role of combining multiple sensors, viz., sensor fusion, to achieve more accurate and usable position and reconstruction information. This project investigates the role of sensor fusion in accurately estimating the pose of a camera for the application of 3D reconstruction of a scene. The first set of experiments is conducted in a motion capture room. These results are assumed as ground truth in order to evaluate the strengths and weaknesses of each sensor and to map their coordinate systems. Then a number of scenarios are targeted where SfM fails. The pose estimates obtained from SfM are replaced by those obtained from other sensors and the 3D reconstruction is completed. Quantitative and qualitative comparisons are made between the 3D reconstruction obtained by using only a camera versus that obtained by using the camera along with a LIDAR and/or an IMU. Additionally, the project also works towards the performance issue faced while handling large data sets of high-resolution images by implementing the system on the Superior high performance computing cluster at Michigan Technological University.