Kalman Filter-based Algorithms for Estimating Depth from Image Sequences

Kalman Filter-based Algorithms for Estimating Depth from Image Sequences (1989)

Cached

Download Links

[www.ri.cmu.edu]
[www.ri.cmu.edu]

Other Repositories/Bibliography

DBLP

by Larry Matthies , Takeo Kanade , Richard Szeliski

Citations:

191 - 23 self

Versions

Version History

Metadata Version 2

User correction supplied by SystemCorrections

Datum	Value	Source
TITLE	Kalman Filter-based Algorithms for Estimating Depth from Image Sequences	user correction - Legacy Corrections
AUTHOR NAME	Larry Matthies	user correction - Legacy Corrections
AUTHOR AFFIL	Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213;; Schlumberger Palo Alto Research, 3340 Hillview Ave., Palo Alto	user correction - Legacy Corrections
AUTHOR ADDR	CA 94304	user correction - Legacy Corrections
AUTHOR NAME	Takeo Kanade	user correction - Legacy Corrections
AUTHOR AFFIL	Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213;; Schlumberger Palo Alto Research, 3340 Hillview Ave., Palo Alto	user correction - Legacy Corrections
AUTHOR ADDR	CA 94304	user correction - Legacy Corrections
AUTHOR NAME	Richard Szeliski	user correction - Legacy Corrections
AUTHOR AFFIL	Digital Equipment Corporation	user correction - Legacy Corrections
AUTHOR ADDR	1 Kendall Square, Building 700, Cambridge, MA 02139	user correction - Legacy Corrections
ABSTRACT	Using known camera motion to estimate depth from image sequences is an important problem in robot vision. Many applications of depth-from-motion, including navigation and manipulation, require algorithms that can estimate depth in an on-line, incremental fashion. This requires a representation that records the uncertainty in depth estimates and a mechanism that integrates new measurements with existing depth estimates to reduce the uncertainty over time. Kalman filtering provides this mechanism. Previous applications of Kalman filtering to depth-from-motion have been limited to estimating depth at the location of a sparse set of features. In this paper, we introduce a new, pixel-based (iconic) algorithm that estimates depth and depth uncertainty at each pixel and incrementally refines these estimates over time. We describe the algorithm and contrast its formulation and performance to that of a feature-based Kalman filtering algorithm. We compare the performance of the two approaches by analyzing their theoretical convergence rates, by conducting quantitative experiments with images of a flat poster, and by conducting qualitative experiments with images of a realistic outdoor-scene model. The results show that the new method is an effective way to extract depth from lateral camera translations. This approach can be extended to incorporate general motion and to integrate other sources of information, such as stereo. The algorithms we have developed, which combine Kalman filtering with iconic descriptions of depth, therefore can serve as a useful and general framework for low-level dynamic vision.	user correction - Legacy Corrections
YEAR	1989	user correction - Legacy Corrections
CITATIONS	29 found	ParsCit 1.0