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To present a Mobile Scanning System for digitizing three‐dimensional (3D) models of real‐world terrain.

A combination of sensors (video, laser range, positioning, orientation) is placed on a mobile platform, which moves past the scene to be digitized. Data fusion from the sensors is performed to construct an accurate 3D model of the target environment.

The developed system can acquire accurate models of real‐world environments in real time, at resolutions suitable for a variety of tasks.

Treating the individual subsystems of the mobile scanning system independently yields a robust system that can be easily reconfigured on the fly for a variety of scanning scenarios.

This paper describes an imaging system that was developed to aid industrial bin picking tasks. The purpose of this system was to provide accurate 3D models of parts and objects in the bin, so that precise grasping operations could be performed. The technology described here is based on two types of sensors: range mapping scanners and video cameras. The geometry of bin contents was reconstructed from range maps and modeled using superquadric representations, providing location and parts surface information that can be employed to guide the robotic arm. Texture was also provided by the video streams and applied to the recovered models. The system is expected to improve the accuracy and efficiency of bin sorting and represents a step toward full automation.

In this paper, we explore the technical challenges to automatically generate computer‐aided design models of existing vehicle parts using laser range imaging techniques. We propose a complete system that integrates data acquisition and model reconstruction. We discuss methods to resolve the occlusion problem and the associated registration problem. We also present our reconstruction algorithm. This range image‐based, computer‐aided reverse engineering system has a potential for faster model reconstruction over traditional reverse engineering technologies. Finally, we present results derived from the system.

This paper seeks to present a novel X‐ray system and associated image segmentation algorithm for imaging the below‐ground root structures of plants.

A matched filter design for segmenting the important root structures from the background clutter in the X‐ray images was presented.

The feasibility of root imaging and the applicability of matched filters to this problem domain have been demonstrated.

This research offers a novel approach over existing methods for in situ monitoring of root structures through the application of matched filters for image segmentation.

The current threats to US security, both military and civilian, have led to an increased interest in the development of technologies to safeguard national facilities such as military bases, federal buildings, nuclear power plants, and national laboratories. As a result, the imaging, robotics, and intelligent systems (IRIS) laboratory at the University of Tennessee has established a research consortium, known as security automation and future electromotive robotics (SAFER), to develop, test, and deploy sensing and imaging systems. In this paper, we describe efforts made to build multi‐perspective mosaics of infrared and color video data for the purpose of under vehicle inspection. It is desired to create a large, high‐resolution mosaic that may be used to quickly visualize the entire scene shot by a camera making a single pass underneath the vehicle. Several constraints are placed on the video data in order to facilitate the assumption that the entire scene in the sequence exists on a single plane. Therefore, a single mosaic is used to represent a single video sequence.

Investigate the use of two imaging‐based methods – coded pattern projection and laser‐based triangulation – to generate 3D models as input to a rapid prototyping pipeline.

Discusses structured lighting technologies as suitable imaging‐based methods. Two approaches, coded‐pattern projection and laser‐based triangulation, are specifically identified and discussed in detail. Two commercial systems are used to generate experimental results. These systems include the Genex Technologies 3D FaceCam and the Integrated Vision Products Ranger System.

Presents 3D reconstructions of objects from each of the commercial systems.

Provides background in imaging‐based methods for 3D data collection and model generation. A practical limitation is that imaging‐based systems do not currently meet accuracy requirements, but continued improvements in imaging systems will minimize this limitation.

Imaging‐based approaches to 3D model generation offer potential to increase scanning time and reduce scanning complexity.

Introduces imaging‐based concepts to the rapid prototyping pipeline.

Aims to develop a robotic platform to autonomously track a moving object

This robotic platform, based on a modular system known as SafeBot, uses two sensors: a visual CCD camera and a laser‐based range sensor. The rigidly mounted camera tracks an object in front of the platform and generates appropriate drive commands to keep the object in view, even if the object itself moves. The range sensor detects other objects as the platform moves to provide real‐time obstacle avoidance while continuously tracking the original object.

The current approach successfully tracks an object, particularly a human subject, and avoids reasonably sized obstacles, but on‐board processing limitations restrict the speed of the object to approximately 5 km/h.

The core technology – a moving object tracked by a mobile robot with real‐time obstacle avoidance – is an integrated system comprising object tracking on a mobile platform and real‐time obstacle avoidance with robotic control. This system is applicable to a variety of automated applications such as inventory management, industrial palette distribution, and intruder surveillance.