Stereophotoclinometry for Navigation - Dr. Eric E. Palmer

Stereophotoclinometry for
       Navigation
        Dr. Eric E. Palmer

What is
            Stereophotoclinometry
•   A suite of tools designed to generate a shape model (Digital Elevation Model)
    using all imagery possible
•   Blends the best parts of Stereo with 2D Photoclinometry to minimize the errors
    of each
      •   Stereo: Sets absolute distance
      •   Photoclinometry: Allows a wider range of emission and illumination
          conditions
•   Solves for topography and albedo, allowing any illumination and observation
    condition to provide useful data

                            Real                  Model

Photoclinometry
         Same surface material (and albedo)
Brightness determined by angle to the Sun (incidence)

How It Works

• What to you do with ……
     Same feature but unrelated angles

What is in common?
Real

Model

We build a world
• Pick a point
• Build a 99x99 pixel
  “maplet” around it
• Align every image
  that intersects this
  maplet
• Solve for slope x,
  slope y and albedo

Generate Small Maps
     Center point determined
            by stereo

                               Interstitial heights by
                                  photoclinometry

•   Combine the maplets to form a whole object
•   Iterate to smooth the maplets until a
    common solution

67P Churyumov–Gerasimenko

Performance
•   Build DEM down to image resolution

      •   In special cases, we can extrapolate by a factor of two

•   Error is “on the order” of the image resolution

      •   SPC registers all image to a mean of one pixel

•   For ORex (at 5km orbit).

      •   Spacecraft uncertainty:

SPC’s improves standard
              stereo
•   Stereo is limited with observation conditions

      •   Maximum stereo angle between images ~ 40°

      •   Requires very similar illumination

•   SPC

      •   Resizes and orthorectifies every image

      •   Generates a model image for every image that is illuminated with
          matching conditions

            •   This step requires topography and albedo

      •   Allows every image to be used to extract topographic information

SPC’s improves standard
         photoclinometry
•   2D solutions — 1D solutions could cause error if the
    line was not allow the maximum slope. SPC avoids
    this because it solves for the entire surface

•   Solve for albedo — Standard photoclinometry
    assumes a uniform albedo. SPC solves for the albedo
    on a pixel-to-pixel basis.

•   Multiple images — SPC uses multiple images taken
    with different phase angles. This reduces the impact
    of noise, cosmic rays, blur and the photometric
    response of the surface.

Sonoita, Arizona
Real Image - 0.6 m/pix   SPC Model - 1.5 m/pix

Karpinskiy Crater
Lunar regional DEMLROC
                   withImage                                               SPCm/px.
                        grid overlay. Image above has been down-sampled to 10  Model

                                                                              72.69° N 166.76° E

Building Features
•   Start with a global model

•   Using nominal flight path, identify
    footprint (with uncertainty) for each
    NavCam image

•   Select features in expected NavCam
    footprint and identify them in existing
    images

    •   Low emission angles provide the best

    •   Suggest 3 to 5 features

        •   Want them spread across the field
            of view

Example of OREx Feature
                (P3808)
Rocks/boulders are good for
humans, but poor choices for
shape models. Small errors
in height result in large errors
when casting shadows

                                   Feature

                                   TAG Site
 3cm/pixel

Source Image

Example P3808

       3D Perspective View
•   Average Correlation 0.8160   Rendered Image
•   38 Images (1.2 to 6cm)
•   125 Pixels wide
•   GSD 3.2 cm

Source Image

           Accuracy
•   Two components that must be correct

     •   3D position in space. This is considered
         the normal concept of accuracy. Is the
         model in the correct location in space.
                                                      Rendered Image
     •   Correlation. To do TRN, the rendered
         images must be similar to the NavCam
         images. If they are not, then the feature
         cannot be uniquely identified.

           •   Failed to identify is poor

           •   Incorrectly identify must be avoided

Quality
•   Correlation scores indication how close the rendered image matches the actual image.

•   Issues impacting correlation score

    •   Large rocks hurt — craters are fine

    •   Albedo features are very useful

    •   Unsampled imagery observing conditions

               0.60                           0.69                      0.78

                        Example Correlation Scores

Types of Images
   “Albedo” Image           Topographic Image
 Incidence angle ~ 0        Incidence angle > 0
Emission Angle - flexible   Emission Angle > 30

The End