4 resultados para Noisy 3D data
em Digital Commons - Michigan Tech
Resumo:
Though 3D computer graphics has seen tremendous advancement in the past two decades, most available mechanisms for computer interaction in 3D are high cost and targeted for industry and virtual reality applications. Recent advances in Micro-Electro-Mechanical-System (MEMS) devices have brought forth a variety of new low-cost, low-power, miniature sensors with high accuracy, which are well suited for hand-held devices. In this work a novel design for a 3D computer game controller using inertial sensors is proposed, and a prototype device based on this design is implemented. The design incorporates MEMS accelerometers and gyroscopes from Analog Devices to measure the three components of the acceleration and angular velocity. From these sensor readings, the position and orientation of the hand-held compartment can be calculated using numerical methods. The implemented prototype is utilizes a USB 2.0 compliant interface for power and communication with the host system. A Microchip dsPIC microcontroller is used in the design. This microcontroller integrates the analog to digital converters, the program memory flash, as well as the core processor, on a single integrated circuit. A PC running Microsoft Windows operating system is used as the host machine. Prototype firmware for the microcontroller is developed and tested to establish the communication between the design and the host, and perform the data acquisition and initial filtering of the sensor data. A PC front-end application with a graphical interface is developed to communicate with the device, and allow real-time visualization of the acquired data.
Resumo:
This thesis represents the overview of hydrographic surveying and different types of modern and traditional surveying equipment, and data acquisition using the traditional single beam sonar system and a modern fully autonomous underwater vehicle, IVER3. During the thesis, the data sets were collected using the vehicles of the Great Lake Research Center at Michigan Technological University. This thesis also presents how to process and edit the bathymetric data on SonarWiz5. Moreover, the three dimensional models were created after importing the data sets in the same coordinate system. In these interpolated surfaces, the details and excavations can be easily seen on the surface models. In this study, the profiles are plotted on the surface models to compare the sensors and details on the seabed. It is shown that single beam sonar might miss some details, such as pipeline and quick elevation changes on the seabed when we compare to the side scan sonar of IVER3 because the single side scan sonar can acquire better resolution. However, sometimes using single beam sonar can save your project time and money because the single beam sonar is cheaper than side scan sonars and the processing might be easier than the side scan data.
Resumo:
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.
Resumo:
The particulate matter distribution (PM) trends that exist in catalyzed particulate filters (CPFs) after loading, passive oxidation, active regeneration, and post loading conditions are not clearly understood. These data are required to optimize the operation of CPFs, prevent damage to the CPFs caused by non-uniform distributions, and develop accurate CPF models. To develop an understanding of PM distribution trends, multiple tests were conducted and the PM distribution was measured in three dimensions using a terahertz wave scanner. The results of this work indicate that loading, passive oxidation, active regeneration, and post loading can all cause non-uniform PM distributions. The density of the PM in the substrate after loading and the amount of PM that is oxidized during passive oxidations and active regenerations affect the uniformity of the distribution. Post loading that occurs after active regenerations result in distributions that are less uniform than post loading that occurs after passive oxidations.