Automated Indoor Modeling
Nikhil Naikal, Avideh Zakhor and John Kua1
Three-dimensional models of objects, sites, buildings, structures, and urban environments made of geometry and texture are important in a variety of civilian and military applications such as training and simulation for disaster management, counteracting terrorism, virtual heritage conservation, virtual museums, historical sites documentation, mapping of hazardous sites and underground tunnels, and modeling of industrial and power plants for design verification and manipulation.
While object modeling has received a great deal of attention in recent years, 3D site modeling, particularly for indoor environments, poses significant challenges. The main objective of this proposal is to design, analyze, and develop architecture and algorithms, as well as associated statistical and mathematical framework for a human-operated, portable, 3D indoor/outdoor modeling system, capable of generating a photo-realistic rendering of the internal structure of multi-story buildings as well as the external structure of a collection of buildings in a campus.
Key technical challenges consist of system architecture, sensor choice, calibration, and error characterization, local and global localization algorithms, sensor integration, registration, and fusion, and complete and accurate coverage of all details. We plan to design and build a prototype system for experimentation purposes, and develop a mathematical framework for online and off-line localization and 6 DOF pose recovery algorithms, taking into account sensor noise, bias, drift, and measurement errors, as well as computational complexity and scalability. In doing so, we need to pay special attention to error analysis, convergence properties, and optimality analysis of the resulting algorithms. We will demonstrate and characterize the speed, accuracy, and scalability performance of our results on a multi-story test building on the UC Berkeley campus.
- C. Fruh and A. Zakhor, "An Automated Method for Large-Scale, Ground-based City Model Acquisition," International Journal of Computer Vision, Vol. 60, No. 1, October 2004, pp. 5-24.
1EECS Research Staff