Range imagery from a laser scanner can be used to provide sufficient information for docking and obstacle avoidance procedures to be performed automatically. Three dimensional model-based computer vision algorithms in development can perform these tasks even with targets which may not be cooperative (that is, objects without special targets or markers to provide unambiguous points). Role, pitch, and yaw of a vehicle can be taken into account as image scanning takes place, so that these can be correlated when the image is converted from egocentric to world coordinated. Other attributes of the sensor, such as the registered reflectance and texture channels, provide additional data sources for algorithm robustness

Beyer, J.

Jacobus, C.

Mitchell, B.

English

NASA Technical Reports Server

Orbital Navigation, Docking, and Obstacle Avoidance
As A Form Of Three Dimensional Model-Based Image Understanding
J. Beyer, C. Jacobus, B. Mitchell
Environmental Research Institute of Michigan
P.O. Box 8618, Ann Arbor, MI 48107
ABSTRACT
Range imagery from a laser scanner developed at ERIM can be used
to provide sufficient information for docking and obstacle avoidance
procedures to be performed automatically. Three dimensional
model-based computer vision algorithms in development at ERIM can
perform these tasks even with targets which may not be cooperative
(that is, objects without special targets or markers to provide
unambiguous location points). Roll, Pitch, and Yaw of vehicle can be
taken into account as image scanning takes place, so that these can be
corrected when the image is converted from egocentric to world
coordinates. Other attributes of the sensor, such as the registered
reflectance and texture channels, provide additional data sources for
algorithm robustness.
i. INTRODUCTION
ERIM has been working towards a laser collision avoidance and
spacecraft docking system over the last year. Our interest in this
project has been motivated by several key events. We have had a long
history of using laser radar techniques for precision robotics control
for manipulation of parts in jumbled environments, for navigation and
obstacle avoidance for vehicle systems (for both factory and natural
environments), and for remote data collection for mapping. We
recently (October 1987) were selected by NASA Code IC to be the second
Center for Commercial Development of Space Automation and Robotics,
and have been tasked with developing the sensing systems component of
the Fairchild Space Company Team's Flight Telerobotic Servicer (for
NASA Goddard).
To support these activities we began working on the design of a
system which will allow imaging and docking with uncooperative
spacecraft (or other material). By uncooperative, we mean craft which
may not have special reflectors or patterns to facilitate the docking
operation. This is in contrast to the work currently underway at
Johnson Space Center to demonstrate a system which requires designed
targets mounted on the spacecraft (i.e. works with cooperative
craft). The concept is based on forming range images from a sensor
mounted on a maneuverable spacecraft (Figure i). These range images
are then processed by a model-based image processing system to find
distinct object locations which can be used to drive a
docking/tracking algorithm.
We discuss how this technology, in two different forms, is
applicable to both the obstacle avoidance and the precision inspection
(and robot control) problems in a Flight Telerobotic Servicer system,
and can be used to validate and analyse large space structures, like
37
https://ntrs.nasa.gov/search.jsp?R=19880014810 2020-03-20T06:54:05+00:00Z
the Space Station.
2. THE THEORY OF DOCKING
Figure 1 shows a schematic form of the basic docking problem. To
be able to maneuver to a given spacecraft, it is necessary to be able
to measure its orientation and motion relative to the maneuverable
platform precisely. In open space (free from other significant
gravitational forces), docking can be directly computed from the
relative orientation and motion parameters. In an orbital
environment, the computations are more complicated, and also require
description of the orbital parameters of one of the spacecrafts.
To determine the relative orientation of a craft, the radial
measurements from a sensor on the maneuvering platform to three or
more known points (Pl, P2, P3) on the craft are sufficient. Figure 2
shows three such measurements, RI, R2, and R3. To calculate the
satellite coordinate system's unit vectors, U, V, W, as a function of
the maneuvering platform's, X, Y, Z (centered at the sensor):
U = (R3 - R2)/abs(R3 - R2)
V = (RI - R3)/abs(Rl - R3)
W=UxV
The direction cosine matrix, D, can be converted into any
equivalent form, such as pitch, roll, yaw, and is computed as follows:
transpose(U)
D = transpose(V)
transpose(W)
The difference in D from measurement to measurement can be used to
estimate changes in orientation (pitch, roll, and yaw) and position
for motion estimation. These calculations can be made for any three
locations Pl, P2, and P3 as long as they are not colinear.
Because of errors in measurement, and because of the possibility
of missing measurements (due to loosing identification of Pl, P2, P3),
it is advisable to feed the the measurements into a Kalman filter.
This provides for continuous prediction of spacecraft motion and
estimation of probable errors in location.
Table i. summarizes the design goals for the Johnson Space
Center cooperative docking experiment. These have been taken by us as
reasonable specifications for the docking sensor, with the exception
of the maximum bearing angle, which we believe should be as close to
180 degrees as possible (to allow docking and obstacle avoidance over
an entire hemisphere), and pitch/yaw which should allow for +/-180
degrees of rotation.
38
Table i. Docking Sensor Requirements and Accuracy
Quantity Measurement Range Measurement Accuracy
Range 0-5 mi 1% of range to 0.016 ft
Range Rate +/-20 ft/s 0.7% of range**i/3 to 0.01 ft/s
Bearing Angle +/-i0 deg 0.05 deg nominally
Bearing Angle Rate +/-i deg/s 0.003 deg/s nominally
Pitch and Yaw +/-45 deg 0.3 deg within i00 ft
Roll +/-180 deg 0.3 deg within i00 ft
Attitude Rate +/-6 deg/s 0.01 deg/s within i00 ft
3. THE SENSOR
ERIM's approach to performing the ranging task involves using a
three dimensional laser ranger. This technique and the associated
technology has been developed and used for obstacle avoidance,
robotics applications, and mapping for over ten years. The three
dimensional ranger is essentially an optical radar, and is shown in
functional form in Figure 3. The laser ranger uses a laser diode
operating at 820nm as its source. This diode is amplitude modulated
and scanned across the field of view using moving mirrors. The beam
is reflected off of the target satellite, and the reflected light is
gathered by the receive optics and focused on an avalanche photodiode.
The signal from the detector has the same frequency as the laser diode
modulation, but displaced in phase. This phase shift is proportional
to range.
To measure the phase precisely, a lower frequency waveform is
mixed with limiter amplified transmitted and received signals, and the
resulting lower frequencies are phase compared digitally. This
technique allows range measurement to be made real time, without post
processing, except for sensor model correction. By taking
measurements in an array, two dimensional images of range data (along
with registered reflectance data) are formed. Figures 4 and 5 show
representative reflectance and range images of part of a shuttle model
(on a non-reflective background to simulated space). Note that range
data is indeterminate where reflectance values are zero (black in the
reflectance data). Figure 6 shows the range data plotted as a three
dimensional surface, after the reflectance channel data is used to
gate accurate range values only.
There are two key issues which make the ranging system required
for this application somewhat different from those previously designed
for vehicle guidance and robotics. The first is the necessity to
achieve prescribed ranging accuracies. The basic signal to noise
relation is:
S/N = Isg**2/(Ishl**2 + Iss**2 + Inep**2)
Where Isg is the signal current from the reflected beam, Ishl is the
shot noise in the detector due to this signal current, Iss is the shot
noise due to solar illumination, and Inep is the detector noise
equivalent power current. The range measurement accuracy is limited
39
by this S/N ratio. Figure 7 shows a set of design curves relating
range error to S/N for targets at several ranges, and demonstrated
some nominal values for an achievable docking system using this
technology.
Another issue is the likely necessity for measuring range over
progressively shorter range distances to higher accuracies. The basic
phase detection-based ranging scheme is limited to range measurement
over fixed ambiguity distances which are determined by the laser
modulation frequency, f, (and the speed of light, c):
Ra = Ambiguity Interval = c/2f
Measurement over varying ambiguity ranges can either be handled
by having programmable modulation sources, or by mixing two
frequencies, f(1) and f(2). If the ambiguity intervals of each are:
Ra(1) = c/2f(1) Ra(2) = c/2f(2)
Then there will be a beat frequency of f(B) and a corresponding
ambiguity interval, Ra(B):
Ra(B) = Ra(1)Ra(2)/(Ra(1) - Ra(2))
4. MODEL-BASED IMAGE PROCESSING
After acquisition of sufficiently accurate range imagery, the
problem of docking becomes one of finding usable (accurately
locatable, non-colinear) satellite locating points. In the case of
the JSC demonstration system, the problem is simplified by mounting
highly reflective, coded targets on the satellite in known
configurations. In this way, each reflector point can be located and
identified without significant image processing being done.
The problem with this approach is that every object may not have
these reflectors mounted on it. For instance, every strut used to
construct Space Station space frames will probably not be marked this
way. If the strut is dropped by either a construction robot or an EVA
astronaut, and retrieval is required, features inherent to the strut
will have to be used to determine its position and orientation for
docking.
To provide this more general capability ERIM has applied a newly
developed model-based vision system, VISTA, developed around the
Cytocomputer highspeed flight qualified image processing system, a
Symbolics 3600 Lisp machine, and algorithms developed for three
dimensional surface identification. VISTA (Figure 8) contains three
phases: i) transformations of images into state-labeled feature maps
using conventional image processing (for instance to group co-planar
adjacent points), 2) transformation of state-labeled maps into
composite symbolic feature maps (in Lisp list form) which describe
features (such as lines, surfaces, and vertices) and the relationships
between them, and 3) identification by matching prototypical
feature-based object models with portions of composite feature
symbolic feature maps. VISTA system software is comprised of the
4O
image processing language C4PL, which supports conventional and
morphological image processing on the Cytocomputer and the
VISTA-WORKBENCH which defines object structures, relations between
features, and the VISTA model matching language (and marcher), for
defining object (in this case satellite) libraries to drive
recognition.
To find and match satellite features, satellite surfaces, and
then surface junctions (or edges and vertices) must be found, grouped
and coded symbolically. These symbolic quantities can then be matched
against pre-coded satellite models in VISTA's library of known
objects. The satellite encoded models are currently hand built, but
will be build from symbolic input constructed from the range data
taken under controlled conditions in the future. The method for
aggregating surfaces is also under development, however two methods
have been implemented previously. The first finds co-planar points by
using local plane estimates, and then the degree of fit of each
neighborhood range point to the estimated planes. If the individual
range points fit the estimated plane well, then the local area is
marked as flat (the flat state). If the points do not fit the plane
well, then the areas is marked as discontinuous. If the range points
jump discontinuously, then the region is marked as a step
discontinuity, otherwise it is left as a roof discontinuity. These
states are then used to extract a composite feature map, and matched
against models for object segmentation, and then satellite (and
therefore, satellite feature point) identification.
A similar surface aggregation algorithm developed by Besl and
Jain is being coded to allow generalization from planar surfaces to
surfaces which are described by more general polynomials. This
enhancement may not be required for satellite docking, because for the
large number of planar surface typically found in man constructed
objects.
5. APPLICATIONS AND FURTHER WORK
This approach to docking is actually a direct application of
three dimensional imaging and model-based image processing to the
satellite location and orientation finding problem. This problem is
also part of the solution to the collision avoidance problem between
controlled multiple robots, between the robots and their workspaces,
and between free-flying objects. These problems are all important for
the Flight Telerobotic Servicer system, and will be addressed as this
project continues.
The automatic generation of VISTA object models is related to the
problem of making a consistent CAD database (composed of high-level
graphical entities, as opposed to simple collections of range points)
from range measurements. We have solved this problem for encoding
complex surfaces as meshes, but must do more work to reliably convert
these meshes into simpler, and more general graphical entities. This
capability allows the accurate capture of solid objects, and can be
used as input to solids modelling system to verify accurate object
mating, without building and fitting mock-ups. As structures in space
become more complex, this capability will be routinely required as
part of subsystem physical checkout.
6. REFERENCES
i. Besl, P. and Jain, R. 1985. Intrinsic and extrinsic surface
characteristics. Proceedings of Computer Vision and Pattern
Recognition Conference (San Francisco, CA, June 9-13), pp.
226-233.
2. Sampson, R., Swonger, C. W., VanAtta, P. 1984. Real Time 3
Dimensional Image Processing for Robot Applications. SME Robots
8, (Detroit, June).
3. Crimmins, T. R., W. M. Brown, 1985. Image Algebra and
Automatic Shape Recognition. IEEE Transactions on Aerospace and
Electronic Systems.
4. Jacobus, C., 1986. Autonomous Delivery Vehicle Systems. USPS
Advanced Technology Conference, (Washington, D.C., September).
5. Mitchell, B., Gillies, A. 1987. A System for Feature-Model-Based
Image Understanding. ERIM Report.
42
/Satellite Satellite
PI
P3 _V P2
Satellite/ U
coordinates W
R3
Docking_
R1
Maneuverable Craft X
Sensor"- --_-I
Y
Figure 1. The Laser Docking System Concept
Z
Figure 2. Laser Range Measur_l_nts
43
Rellectance i'_
I/ r
_,,,_ Scanning _':_>?'; "_ Osc ao, [-'.
I i'}_"_:":'_";:;_"_i_::l\_" \ _ DigilizedRange
Image Frzune 0
3G0O-0o 360o.0o
Figure 3. 3D Scanner Block Diagram
Figure 4. Shuttle Reflectance Image Figure 5. Shuttle Range Image
44
Figure 6. Shuttle Range Image Plotted From Perspective
1000. • - _ t I I 1
o x
100. Key."
0 R9 " ,1 it
--" O R9 " 1 ft
o 10. _ R9 - lOft
® X Rg - 100 ft
'_ _ Rg - 3280ft
c_
1. 0 Rg - 12,000 ft
O Rg - 22.000 ft
0.1
0.01
O. 20. 40, GO. 80. 100. 120,
SNR, dB
Figure 7. RMS Range Error versus S_R For a Typical Design
45
11- _l
I C_m_'l_ll°n I Feail.ire
Moclel _ Mo_els@ °°oo0j@ ,=0 0 0 0 o_amcC_ rnpH,tl_ri
"i_ Q fl,1185
Composile ,_odel S_eclhc J
_I_ Malc_'_er IKnowl_e tlulesiI
C,_mputar Fealure Syn'_r_s
Ma,as _ier
FIGURE 8: VISTA System Overview
46


Orbital navigation, docking and obstacle avoidance as a form of three dimensional model-based image understanding

Abstract

Similar works

Full text

Available Versions

NASA Technical Reports Server