The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XL-3/W2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W2, 39–45, 2015
https://doi.org/10.5194/isprsarchives-XL-3-W2-39-2015
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W2, 39–45, 2015
https://doi.org/10.5194/isprsarchives-XL-3-W2-39-2015

  10 Mar 2015

10 Mar 2015

RELATIVE POSE ESTIMATION USING IMAGE FEATURE TRIPLETS

T. Y. Chuang, F. Rottensteiner, and C. Heipke T. Y. Chuang et al.
  • Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, Germany

Keywords: Three-view Geometry, Line Triplet Matching, Relative Orientation, Feature Integration

Abstract. A fully automated reconstruction of the trajectory of image sequences using point correspondences is turning into a routine practice. However, there are cases in which point features are hardly detectable, cannot be localized in a stable distribution, and consequently lead to an insufficient pose estimation. This paper presents a triplet-wise scheme for calibrated relative pose estimation from image point and line triplets, and investigates the effectiveness of the feature integration upon the relative pose estimation. To this end, we employ an existing point matching technique and propose a method for line triplet matching in which the relative poses are resolved during the matching procedure. The line matching method aims at establishing hypotheses about potential minimal line matches that can be used for determining the parameters of relative orientation (pose estimation) of two images with respect to the reference one; then, quantifying the agreement using the estimated orientation parameters. Rather than randomly choosing the line candidates in the matching process, we generate an associated lookup table to guide the selection of potential line matches. In addition, we integrate the homologous point and line triplets into a common adjustment procedure. In order to be able to also work with image sequences the adjustment is formulated in an incremental manner. The proposed scheme is evaluated with both synthetic and real datasets, demonstrating its satisfactory performance and revealing the effectiveness of image feature integration.