Editing Scale-invariant feature transform (section)

=== Robot localization and mapping ===
In this application,<ref name="Se2001" /> a trinocular stereo system is used to determine 3D estimates for keypoint locations. Keypoints are used only when they appear in all 3 images with consistent disparities, resulting in very few outliers. As the robot moves, it localizes itself using feature matches to the existing 3D map, and then incrementally adds features to the map while updating their 3D positions using a [[Kalman filter]]. This provides a robust and accurate solution to the problem of robot localization in unknown environments. Recent 3D solvers leverage the use of keypoint directions to solve trinocular geometry from three keypoints<ref name="SIFTOrientationTrifocal">{{cite arXiv
 |last1=Fabbri |first1=Ricardo
 |last2=Duff |first2=Timothy
 |last3=Fan |first3=Hongyi
 |last4=Regan |first4=Margaret
 |last5=de Pinho |first5=David
 |last6=Tsigaridas |first6=Elias
 |last7=Wampler |first7=Charles
 |last8=Hauenstein |first8=Jonathan
 |last9=Kimia |first9=Benjamin
 |last10=Leykin |first10=Anton
 |last11=Pajdla |first11=Tomas
 |date=23 Mar 2019
 |title=Trifocal Relative Pose from Lines at Points and its Efficient Solution
 |eprint=1903.09755
 |class=cs.CV
}}</ref> and absolute pose from only two keypoints,<ref name="SIFTOrientationPose">{{cite book
 |last1=Fabbri |first1=Ricardo
 |last2=Giblin |first2=Peter
 |last3=Kimia |first3=Benjamin
 |title=Computer Vision – ECCV 2012
 |chapter=Camera Pose Estimation Using First-Order Curve Differential Geometry
 |date=2012
 |volume=7575 |pages=231–244
 |url=https://rfabbri.github.io/stuff/fabbri-giblin-kimia-eccv2012-final-ext.pdf
 |doi=10.1007/978-3-642-33765-9_17
 |series=Lecture Notes in Computer Science
 |isbn=978-3-642-33764-2
 |s2cid=15402824
 }}</ref> an often disregarded but useful measurement available in SIFT. These orientation measurements reduce the number of required correspondences, further increasing robustness exponentially.