10,497 research outputs found
Reflectance Transformation Imaging (RTI) System for Ancient Documentary Artefacts
This tutorial summarises our uses of reflectance transformation imaging in archaeological contexts. It introduces the UK AHRC funded project reflectance Transformation Imaging for Anciant Documentary Artefacts and demonstrates imaging methodologies
Kinect Range Sensing: Structured-Light versus Time-of-Flight Kinect
Recently, the new Kinect One has been issued by Microsoft, providing the next
generation of real-time range sensing devices based on the Time-of-Flight (ToF)
principle. As the first Kinect version was using a structured light approach,
one would expect various differences in the characteristics of the range data
delivered by both devices. This paper presents a detailed and in-depth
comparison between both devices. In order to conduct the comparison, we propose
a framework of seven different experimental setups, which is a generic basis
for evaluating range cameras such as Kinect. The experiments have been designed
with the goal to capture individual effects of the Kinect devices as isolatedly
as possible and in a way, that they can also be adopted, in order to apply them
to any other range sensing device. The overall goal of this paper is to provide
a solid insight into the pros and cons of either device. Thus, scientists that
are interested in using Kinect range sensing cameras in their specific
application scenario can directly assess the expected, specific benefits and
potential problem of either device.Comment: 58 pages, 23 figures. Accepted for publication in Computer Vision and
Image Understanding (CVIU
Building with Drones: Accurate 3D Facade Reconstruction using MAVs
Automatic reconstruction of 3D models from images using multi-view
Structure-from-Motion methods has been one of the most fruitful outcomes of
computer vision. These advances combined with the growing popularity of Micro
Aerial Vehicles as an autonomous imaging platform, have made 3D vision tools
ubiquitous for large number of Architecture, Engineering and Construction
applications among audiences, mostly unskilled in computer vision. However, to
obtain high-resolution and accurate reconstructions from a large-scale object
using SfM, there are many critical constraints on the quality of image data,
which often become sources of inaccuracy as the current 3D reconstruction
pipelines do not facilitate the users to determine the fidelity of input data
during the image acquisition. In this paper, we present and advocate a
closed-loop interactive approach that performs incremental reconstruction in
real-time and gives users an online feedback about the quality parameters like
Ground Sampling Distance (GSD), image redundancy, etc on a surface mesh. We
also propose a novel multi-scale camera network design to prevent scene drift
caused by incremental map building, and release the first multi-scale image
sequence dataset as a benchmark. Further, we evaluate our system on real
outdoor scenes, and show that our interactive pipeline combined with a
multi-scale camera network approach provides compelling accuracy in multi-view
reconstruction tasks when compared against the state-of-the-art methods.Comment: 8 Pages, 2015 IEEE International Conference on Robotics and
Automation (ICRA '15), Seattle, WA, US
A novel low-cost autonomous 3D LIDAR system
Thesis (M.S.) University of Alaska Fairbanks, 2018To aid in humanity's efforts to colonize alien worlds, NASA's Robotic Mining Competition pits universities against one another to design autonomous mining robots that can extract the materials necessary for producing oxygen, water, fuel, and infrastructure. To mine autonomously on the uneven terrain, the robot must be able to produce a 3D map of its surroundings and navigate around obstacles. However, sensors that can be used for 3D mapping are typically expensive, have high computational requirements, and/or are designed primarily for indoor use. This thesis describes the creation of a novel low-cost 3D mapping system utilizing a pair of rotating LIDAR sensors, attached to a mobile testing platform. Also, the use of this system for 3D obstacle detection and navigation is shown. Finally, the use of deep learning to improve the scanning efficiency of the sensors is investigated.Chapter 1. Introduction -- 1.1. Purpose -- 1.2. 3D sensors -- 1.2.1. Cameras -- 1.2.2. RGB-D Cameras -- 1.2.3. LIDAR -- 1.3. Overview of Work and Contributions -- 1.4. Multi-LIDAR and Rotating LIDAR Systems -- 1.5. Thesis Organization. Chapter 2. Hardware -- 2.1. Overview -- 2.2. Components -- 2.2.1. Revo Laser Distance Sensor -- 2.2.2. Dynamixel AX-12A Smart Serial Servo -- 2.2.3. Bosch BNO055 Inertial Measurement Unit -- 2.2.4. STM32F767ZI Microcontroller and LIDAR Interface Boards -- 2.2.5. Create 2 Programmable Mobile Robotic Platform -- 2.2.6. Acer C720 Chromebook and Genius Webcam -- 2.3. System Assembly -- 2.3.1. 3D LIDAR Module -- 2.3.2. Full Assembly. Chapter 3. Software -- 3.1. Robot Operating System -- 3.2. Frames of Reference -- 3.3. System Overview -- 3.4. Microcontroller Firmware -- 3.5. PC-Side Point Cloud Fusion -- 3.6. Localization System -- 3.6.1. Fusion of Wheel Odometry and IMU Data -- 3.6.2. ArUco Marker Localization -- 3.6.3. ROS Navigation Stack: Overview & Configuration -- 3.6.3.1. Costmaps -- 3.6.3.2. Path Planners. Chapter 4. System Performance -- 4.1. VS-LIDAR Characteristics -- 4.2. Odometry Tests -- 4.3. Stochastic Scan Dithering -- 4.4. Obstacle Detection Test -- 4.5. Navigation Tests -- 4.6. Detection of Black Obstacles -- 4.7. Performance in Sunlit Environments -- 4.8. Distance Measurement Comparison. Chapter 5. Case Study: Adaptive Scan Dithering -- 5.1. Introduction -- 5.2. Adaptive Scan Dithering Process Overview -- 5.3. Coverage Metrics -- 5.4. Reward Function -- 5.5. Network Configuration -- 5.6. Performance and Remarks. Chapter 6. Conclusions and Future Work -- 6.1. Conclusions -- 6.2. Future Work -- 6.3. Lessons Learned -- References
Heritage Recording and 3D Modeling with Photogrammetry and 3D Scanning
The importance of landscape and heritage recording and documentation with optical remote sensing sensors is well recognized at international level. The continuous development of new sensors, data capture methodologies and multi-resolution 3D representations, contributes significantly to the digital 3D documentation, mapping, conservation and representation of landscapes and heritages and to the growth of research in this field. This article reviews the actual optical 3D measurement sensors and 3D modeling techniques, with their limitations and potentialities, requirements and specifications. Examples of 3D surveying and modeling of heritage sites and objects are also shown throughout the paper
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