The Ames MER microscopic imager toolkit

The Mars Exploration Rovers, spirit and opportunity, have spent several successful months on Mars, returning gigabytes of images and spectral data to scientists on Earth. One of the instruments on the MER rovers, the Athena microscopic imager (MI), is a fixed focus, megapixel camera providing a /spl plusmn/3mm depth of field and a 31/spl times/31 mm field of view at a working distance of 63 mm from the lens to the object being imaged. In order to maximize the science return from this instrument, we developed the Ames MI toolkit and supported its use during the primary mission. The MI toolkit is a set of programs that operate on collections of MI images, with the goal of making the data more understandable to the scientists on the ground. Because of the limited depth of field of the camera, and the often highly variable topography of the terrain being imaged, MI images of a given rock are often taken as a stack, with the instrument deployment device (IDD) moving along a computed normal vector, pausing every few millimeters for the MI to acquire an image. The MI toolkit provides image registration and focal section merging, which combine these images to form a single, maximally in-focus image, while compensating for changes in lighting as well as parallax due to the motion of the camera. The MI toolkit also provides a 3D reconstruction of the surface being imaged using stereo and can embed 2D MI images as texture maps into 3D meshes produced by other imagers on board the rover to provide context. The 2D images and 3D meshes output from the toolkit are easily viewed by scientists using other mission tools, such as Viz or the MI browser. This paper describes the MI toolkit in detail, as well as our experience using it with scientists at JPL during the primary MER mission.

[1]  Jeff A. Bilmes,et al.  A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models , 1998 .

[2]  P H Smith,et al.  Textures of the soils and rocks at Gusev Crater from Spirit's Microscopic Imager. , 2004, Science.

[3]  Miles J. Johnson,et al.  Athena Microscopic Imager investigation , 2003 .

[4]  D. Greig,et al.  Exact Maximum A Posteriori Estimation for Binary Images , 1989 .

[5]  D. Gennery Least-Squares Camera Calibration Including Lens Distortion and Automatic Editing of Calibration Points , 2001 .

[6]  Olga Veksler,et al.  Fast approximate energy minimization via graph cuts , 2001, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[7]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Maria Bualat,et al.  Virtual Reality Interfaces for Visualization and Control of Remote Vehicles , 2001, Auton. Robots.

[9]  G. B. Smith,et al.  Preface to S. Geman and D. Geman, “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images” , 1987 .

[10]  Randy Sargent,et al.  Terrain model registration for single cycle instrument placement , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[11]  Carol R. Stoker,et al.  Analyzing Pathfinder data using virtual reality and superresolved imaging , 1999 .

[12]  D. Gennery,et al.  Calibration and Orientation of Cameras in Computer Vision , 2001 .