Calibration and 3D measurement from Martian terrain images

An on-Mars calibration procedure of a planetary lander vision system is presented. It is based on developments in computer vision. The calibration is retrieved from the images of the Mars terrain. The procedure is based on the relations between multiple views of the same scene. It allows us to retrieve the calibration on Mars without any additional requirements on the system. Once the system has been calibrated, the same images can be used to estimate a digital elevation map (DEM) of the environment around the lander. The images are first processed pairwise using a stereo algorithm yielding sub-pixel disparity maps. An algorithm was developed to efficiently extract a DEM from the disparity maps. The DEM can be generated at any desired resolution (interpolating if necessary) and occlusions are easily taken into account.

[1]  Lutz Falkenhagen.  Hierarchical Block-Based Disparity Estimation Considering Neighbourhood Constraints , 1997 .

[2]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[3]  Andrew Zisserman,et al.  Applications of Invariance in Computer Vision , 1993, Lecture Notes in Computer Science.

[4]  Thomas S. Huang,et al.  Theory of Reconstruction from Image Motion , 1992 .

[5]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[6]  Rangachar Kasturi,et al.  Machine vision , 1995 .

[7]  Reinhard Koch,et al.  Automatische Oberflächenmodellierung starrer dreidimensionaler Objekte aus stereoskopischen Rundum-Ansichten , 1997 .

[8]  Reinhard Koch,et al.  A simple and efficient rectification method for general motion , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[9]  Reinhard Koch,et al.  Metric 3D Surface Reconstruction from Uncalibrated Image Sequences , 1998, SMILE.

[10]  Ian D. Reid,et al.  Self-calibration of a stereo rig in a planar scene by data combination , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[11]  Reinhard Koch,et al.  Self-Calibration and Metric Reconstruction Inspite of Varying and Unknown Intrinsic Camera Parameters , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[12]  Roger Y. Tsai,et al.  A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses , 1987, IEEE J. Robotics Autom..

[13]  R. Rieder,et al.  Nanokhod: A miniature instrument deployment device with instrumentation for chemical, mineralogical and geological analysis of planetary surfaces, for use in connection with fixed planetary surface stations , 1995 .

[14]  I. Reid,et al.  Metric calibration of a stereo rig , 1995, Proceedings IEEE Workshop on Representation of Visual Scenes (In Conjunction with ICCV'95).

[15]  Charles T. Loop,et al.  Computing rectifying homographies for stereo vision , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[16]  Ingemar J. Cox,et al.  A Maximum Likelihood Stereo Algorithm , 1996, Comput. Vis. Image Underst..