Recursive Motion and Structure Estimation with Complete Error Characterization
- Creators
- Soatto, S.
- Perona, P.
- Frezza, R.
- Picci, G.
Abstract
We present an algorithm that perfom recursive estimation of ego-motion andambient structure from a stream of monocular Perspective images of a number of feature points. The algorithm is based on an Extended Kalman Filter (EKF) that integrates over time the instantaneous motion and structure measurements computed by a 2-perspective-views step. Key features of our filter are (I) global observability of the model, (2) complete on-line characterization of the uncertainty of the measurements provided by the two-views step. The filter is thus guaranteed to be well-behaved regardless of the particular motion undergone by the observel: Regions of motion space that do not allow recovery of structure (e.g. pure rotation) may be crossed while maintaining good estimates of structure and motion; whenever reliable measurements are available they are exploited. The algorithm works well for arbitrary motions with minimal smoothness assumptions and no ad hoc tuning. Simulations are presented that illustrate these characteristics.
Additional Information
© 1993 IEEE. Issue Date: 15-17 Jun 1993; Meeting Date: 15 Jun 1993 - 17 Jun 1993. Research sponsored by NSF grant n. 51 115, an AT & T Foundation technical special purpose grant, a donation by Dr. Tongsoo Park, and by AS1 grant n. 92-RS-103.Attached Files
Published - SOAcvpr93.pdf
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Additional details
- Eprint ID
- 29657
- Resolver ID
- CaltechAUTHORS:20120308-151801993
- 51115
- NSF
- AT&T Foundation technical special purpose grant
- Dr. Tongsoo Park
- 92-RS-103
- ASI
- Created
-
2012-03-09Created from EPrint's datestamp field
- Updated
-
2021-11-09Created from EPrint's last_modified field
- Other Numbering System Name
- INSPEC Accession Number
- Other Numbering System Identifier
- 4823680