CRII: RI: High-speed Vision-based Motion Estimation (NSF IIS-1464420)
Jonathan Ventura, Principal Investigator
May 1, 2015 - April 30, 2018 (Estimated)
Amount awarded to date: $174,802.00
Device motion estimation is a key component of spatially-aware systems such as mobile robots and virtual or augmented reality displays. Integration of motion estimates over time, or dead reckoning, is typically used to track the position and orientation of the device in space between intermittent absolute position measurements. Vision-based motion estimation is a passive, low-cost approach that provides high accuracy. This project develops faster methods for vision-based motion estimation.
The motivation for this work is the basic insight that increasing the speed of motion estimation leads to a "virtuous cycle" of system improvement: by computing the motion estimate more quickly, the camera can be operated a higher rate; this in turns leads to less motion between successive frames, enabling faster computations. The speedups achieved in the current preliminary work rely on two techniques: approximation of the motion model; and reduction of fundamental geometric problem to a smaller form which is quicker to solve. By investigating related problems, we aim to determine an entire class of visual motion estimation problems that can be efficiently solved in a similar way.
- Bill Michael, Ph.D. Student
- Uma Maheswari Chinta, M.S. Student
- Kristen Gearhart, Undergraduate Researcher
- Jacob Hill, Undergraduate Researcher
- Hari Sridhar, Undergraduate Researcher
Jonathan Ventura, Clemens Arth and Vincent Lepetit. "An Efficient Minimal Solution for Multi-Camera Motion," International Conference on Computer Vision (ICCV), 2015.
Jonathan Ventura. "Structure from Motion on a Sphere," European Conference on Computer Vision (ECCV), 2016.
Stefanie Zollmann, Christian Poglitsch and Jonathan Ventura. "VISGIS: Dynamic Situated Visualization for Geographic Information Systems," Image and Vision Computing New Zealand (IVCNZ), 2016 International Conference on, 2016.