PDS_VERSION_ID = PDS3 OBJECT = DATA_SET DATA_SET_ID = "MPFL-M-IMP-5-3DPOSITION-V1.0" OBJECT = DATA_SET_INFORMATION DATA_SET_NAME = "MPF LANDER MARS IMP STEREO-DERIVED 3D POSITIONS V1.0" DATA_SET_COLLECTION_MEMBER_FLG = "N" DATA_OBJECT_TYPE = "TABLE" START_TIME = "N/A" STOP_TIME = "N/A" DATA_SET_RELEASE_DATE = 2003-10-01 PRODUCER_FULL_NAME = "CAROL STOKER" CITATION_DESC = "Stoker, C., and S. Slavney, Imager for Mars Pathfinder Stereo-Derived 3D Positions, MPFL-M-IMP-5-3DPOSITION-V1.0, NASA Planetary Data System, 2003." DETAILED_CATALOG_FLAG = "N" ARCHIVE_STATUS = "LOCALLY ARCHIVED" DATA_SET_TERSE_DESC = "Three-dimensional position information for pixels in IMP (Imager for Mars Pathfinder) stereo-pair images." DATA_SET_DESC = " Data Set Overview ================= This data set represents the primary results of three dimensional (3-D) modeling of the Mars Pathfinder landing site using data from the Imager for Mars Pathfinder camera. The camera system is described by Smith et al. [SMITHETAL1997A, SMITHETAL1997B]. It consisted of a stereo imager pair located on a pan and tilt platform. Each imager of the pair was equipped with a filter wheel so that the camera set could image the landscape in 15 narrow-band filters. This data set consists of a set of tables. Each table contains 3-D object position information in the form of a Cartesian (x,y,z) coordinate in units of meters corresponding to each pixel in an IMP EDR stereo pair acquired in the 670 nm filter. The coordinates are deduced using an automated machine vision algorithm that correlates features between the left and right images of stereo pairs to determine their disparity (difference in image position between the left and right eye) then computes their 3-D object position taking into account the camera pointing and stereo optics. The computer algorithm is described by Stoker et al. [STOKERETAL1999] and summarized below. Stereo model products (and corresponding tables) have been produced for two IMP Pathfinder data sets acquired in stereo in the 670 nm filter. The IMP data sets are described by Gaddis et al. [GADDISETAL1999]. The stereo data sets that were analyzed are called the Monster Pan and the Super Pan. The Monster Pan was a complete stereo panorama of the Pathfinder landing site acquired early in the mission (sols 3-6). The monster pan images in the 670 nm filter were compressed using lossy JPEG compression (6:1 compression factor) and the image to image overlap in the panoramic product was relatively low. The Super Pan was designed to produce a full panorama of the landing site with low compression ratio in all 15 narrow-band filters and the 670 nm stereo filter set was losslessly compressed using Rice compression. It was designed with increased frame-to-frame overlap relative to the Monster Pan to assist with automated matching between images and insure gap-free stereo coverage. The Super Pan represented a large data volume and was acquired over an 8 week period from sols 13 to 80. It was 83% complete when the mission ended. While incomplete, the 3-D reconstructions from the Super Pan images are somewhat better than for the Monster Pan due to the increased image overlap and lower image compression. Parameters ========== Each table entry consists of a Cartesian coordinate corresponding to the object position computed for each pixel of the left member of a stereo pair for which a model solution was obtained. The origin of the coordinate system for the values provided in these tables is at the intersection of the camera elevation and azimuth axes. The X axis is aligned with north so that +X values are north of the origin, the Y axis is aligned with west so that +Y values are west of the origin, and the +Z direction is up. Other parameters which enter into the stereo reconstruction are described below along with a description of the Stereo Pipeline algorithm. Processing ========== The computer program which produces the 3-D reconstruction is called the Ames Stereo Pipeline [STOKERETAL1999]. The input to the stereo matching algorithm consists of raw EDR images from an IMP stereo pair. Results are better if images are used that have not been flat field corrected or photometrically calibrated because these processes resample the pixel information. The first stage in the Stereo Pipeline algorithm is called the 'preprocessing' stage and involves preparing the input stereo pair to improve the correlation in the later stages. First, a linear stretch is applied to normalize the image intensity between the left and right members of the stereo pair. This is needed because the correlation algorithm works by matching the intensity values between the image pairs. Then, a uni-directional Sobel edge enhancement technique [BAXES1994] is applied. Next calculations are performed to correct for translational, rotational, and pixel-scale differences between the left and right eyes. The next stage of processing in the Stereo Pipeline is to correlate the features in the images between the left and right cameras. The result of this stage is a disparity calculation for each pixel in the image pair. A texture-based sum-of-absolute-difference (SOAD) correlation algorithm is used and the consistency of each match is validated by doing both a correlation and cross-correlation. This almost eliminates matches between wrong local figures. A small subframe of the image surrounding a considered pixel, called the kernel, is selected from one member of the stereo pair. The kernel is slid over the other image of the pair by a step of one pixel at a time, a subtraction is performed, and the elements of the resulting matrix are summed. This procedure is used to find the position of the most similar portion of the test image with the kernel. Three correlation passes, using different sized kernels, are used to improve both computational speed and accuracy. The same correlation algorithm, with different parameters, is used for all three passes. The first pass of the correlator is used to bound the disparity range of the image. It uses a small kernel and searches across the complete range of possible disparity values. For this first pass, a relatively low rate of correlations are found, but these are used to limit the search space of the disparity for the next pass. The second correlation pass uses a larger kernel which results in a high percentage of pixels being matched. In the final (third) pass, the disparity search is constrained to the neighborhood of the disparity calculated in the previous pass. Ideally, a small kernel size is preferred for this pass because the disparity value assigned to the pixel is the average over the kernel. Kernel size for the second and third pass are user selectable. For the models published here, kernel size was interactively varied to minimize the amount of pixel-to-pixel variance in computed 3-D position. High variance results from errors in the estimate of disparity. Small errors in the estimate of disparity can lead to large errors in the estimate of position along the camera's line of site. The Kernel size for the second and third correlation pass is a user defined quantity of n columns by m rows. Values used for this data set were 14x14 pixels (second pass) and 27x27 pixels (third pass). The correlation stage is followed by a filtering stage that removes 'outliers'-- disparity values much different than those in the nearby area. Next, gaps in the disparity map are filled. Gaps are places which had no match, inconsistent cross-correlations, or outlier disparities. Some gaps are the result of real-world discontinuities in surface shape, such as the occluding boundaries of rocks in the terrain. In order to retain these boundaries in the map, gaps occurring at large discontinuities are filled with the minimum disparity value (corresponding to the point furthest from the camera) in the gap neighborhood. Gaps in regions with small disparity variance are more likely due to a smooth, texture free surface. These gaps can be filled by interpolation or set to zero. In the models published here they are set to zero to avoid confusing them with values computed by the algorithm. The next processing stage derives 3-D position points from disparity values. Each pixel is projected along a vector defined by the (line, sample) coordinate of the pixel and the nodal point of the camera to a distance consistent with its disparity. This intersection point is the object coordinate. Then, using the camera pan and tilt angle, the object coordinates are rotated to the lander coordinate system. This computation is repeated for each pixel of the stereo pair to get a set of object points. These object points, in tabular form, are the data set provided. Ancillary Data ============== The data are referenced to raw IMP EDR images. These will be required for interpretation of the 3-D model data. " CONFIDENCE_LEVEL_NOTE = " Data Coverage and Quality ========================= As discussed above, the 3-D position information is deduced matching brightness patterns in the left and right eyes of the stereo pair. When no match is found, or inconsistent matches found in the correlation and cross correlation, no disparity is calculated and a value of zero is assigned to the Cartesian coordinate (X=Y=Z=0) in the table. Thus, zero values in the table indicate that the stereo matching algorithm did not yield a good solution at that location. Confidence Level and Limitations ================================ For the Mars Pathfinder IMP camera data sets, the error in the 3-D position of an object point in the model comes from the following sources: 1) The uncertainty in the azimuth and elevation of the camera leads to uncertainty in the 3-D model position. According to the IMP calibration report [CROWEETAL1996] the pointing error acts in a plane perpendicular to the camera optical axis. This error is a linear function of the camera-point distance and is within +/- 2.7% in azimuth and +/- 1.2% in elevation of the absolute position of the point (assuming a pan error of +/- 1.5 degrees and a tilt error of +/- 0.65 degrees. These are worst case values due to backlash in the camera motors. Of the uncertainty sources, this is the largest, but the camera pointing uncertainty affects all points from one stereo pair equally as a solid body. This source of error can be minimized by determining actual camera pointing after the fact by using tiepoints between stereo pairs. The United States Geological Survey Astrogeology Branch, under the direction of R. Kirk, undertook a project to provide improved camera pointing information using a control network for the site and bundle adjustment. This procedure is described by Kirk et al. [KIRKETAL2001]. The values they determined were substituted for surface based instrument azimuth and elevation for the instrument telemetry values provided in the original EDR headers. Inspection of the results showed that using these values produced a noticeable improvement in how well models from adjacent images fit together. They also computed values for left toe-in (-13.732 radians), right toe-in (24.63 radians) and boresight angles (1.116 radians) that are different from those published by the IMP camera team [CROWEETAL1996]. We also used these values in our computations. 2) Uncertainty in the computed camera-point distance results from the disparity computation method. For any pixel, the computed disparity represents an average over the Kernel for the final correlation pass. Smaller Kernel sizes lead to a high percentage of false correlations. Thus the models appear more noisy. Even though a disparity point is assigned to each pixel, the real resolution of the model is a function of the Kernel size in the final pass. 3) Image resolution limits stereo matching precision. Subpixel disparity is not computed by the algorithm. 4) The stereo images of the Monster Pan were compressed using lossy JPEG compression. High correlation rates are achieved even with the compressed data of the Monster Pan but the results are clearly noisier (defined as pixel to pixel variance in 3-D position computed by the algorithm) than for the losslessly compressed Super Pan. As discussed above, this variance is due to errors in the estimated disparity. Compression artifacts result in a higher percentage of false matches." END_OBJECT = DATA_SET_INFORMATION OBJECT = DATA_SET_TARGET TARGET_NAME = "MARS" END_OBJECT = DATA_SET_TARGET OBJECT = DATA_SET_HOST INSTRUMENT_HOST_ID = "MPFL" INSTRUMENT_ID = "IMP" END_OBJECT = DATA_SET_HOST OBJECT = DATA_SET_REFERENCE_INFORMATION REFERENCE_KEY_ID = "BAXES1994" END_OBJECT = DATA_SET_REFERENCE_INFORMATION OBJECT = DATA_SET_REFERENCE_INFORMATION REFERENCE_KEY_ID = "CROWEETAL1996" END_OBJECT = DATA_SET_REFERENCE_INFORMATION OBJECT = DATA_SET_REFERENCE_INFORMATION REFERENCE_KEY_ID = "GADDISETAL1999" END_OBJECT = DATA_SET_REFERENCE_INFORMATION OBJECT = DATA_SET_REFERENCE_INFORMATION REFERENCE_KEY_ID = "KIRKETAL2001" END_OBJECT = DATA_SET_REFERENCE_INFORMATION OBJECT = DATA_SET_REFERENCE_INFORMATION REFERENCE_KEY_ID = "SMITHETAL1997A" END_OBJECT = DATA_SET_REFERENCE_INFORMATION OBJECT = DATA_SET_REFERENCE_INFORMATION REFERENCE_KEY_ID = "SMITHETAL1997B" END_OBJECT = DATA_SET_REFERENCE_INFORMATION OBJECT = DATA_SET_REFERENCE_INFORMATION REFERENCE_KEY_ID = "STOKERETAL1999" END_OBJECT = DATA_SET_REFERENCE_INFORMATION END_OBJECT = DATA_SET END