Robotic 
Bayes Networks on Ice: 
Search for Antarctic Meteorites 
Liam Pedersen*, Dimi Apostolopoulos, Red Whittaker 
Robotics Institute 
Carnegie Mellon University 
Pittsburgh, PA 15213 
{pedersen+, dalv, red}ri. cmu. edu 
Abstract 
A Bayes network based classifier for distinguishing terrestrial 
rocks from meteorites is implemented onboard the Nomad robot. 
Equipped with a camera, spectrometer and eddy current sensor, this 
robot searched the ice sheets of Antarctica and autonomously made 
the first robotic identification of a meteorite, in January 2000 at the 
Elephant Moraine. This paper discusses rock classification from a 
robotic platform, and describes the system onboard Nomad. 
1 Introduction 
Figure 1 : Human meteorite search with snowmobiles on the Antarctic ice 
sheets, and on foot in the moraines. 
Antarctica contains the most fertile meteorite hunting grounds on Earth. The 
pristine, dry and cold environment ensures that meteorites deposited there are 
preserved for long periods. Subsequent glacial flow of the ice sheets where they 
land concentrates them in particular areas. To date, most meteorites recovered 
throughout history have been done so in Antarctica in the last 20 years. 
Furthermore, they are less likely to be contaminated by terrestrial compounds. 
* http ://www.cs.cmu.edu/-pedersen 
Meteorites are of interest to space scientists because, with the exception of the 
Apollo lunar samples, they are the sole source of extra-terrestrial material and a 
window on the early evolution of the solar system. The identification of Martian 
and lunar meteorite samples, and the (controversial) evidence of fossil bacteria in 
the former underscores the importance of systematically retrieving as many samples 
as possible. 
Currently, Antarctic meteorite samples are collected by human searchers, either on 
foot, or on snowmobiles, who systematically search an area and retrieve samples 
according to strict protocols. In certain blue ice fields the only rocks visible are 
meteorites. At other places (moraines - areas where the ice flow brings rocks to the 
surface) searchers have to contend with many terrestrial rocks (Figure 1). 
1.1 Robotic search for Antarctic meteorites 
color 
camera 
reflectance 
spectrometer 
Figure 2 : Nomad robot, equipped with scientific instruments, 
investigates a rock in Antarctica. 
With the goal of autonomously search for meteorites in Antarctica, Carnegie Mellon 
University has built and demonstrated [1] a robot, Nomad (Figure 2), capable of 
long duration missions in harsh environments. Nomad is equipped with a color 
camera on a pan-tilt platform to survey the ice for rocks and acquire close up images 
of any candidate objects, and a manipulator arm to place the fiber optic probe of a 
specially designed visible light reflectance spectrometer over a sample. The 
manipulator arm can also place other sensors, such a metal detector. 
The eventual goal, beyond Antarctic meteorite search, is to develop technologies for 
extended robotic exploration of remote areas, including planetary surfaces. One 
particular technology is the capacity to carry out autonomous science, including 
autonomous geology and the ability to recognize a broad range of rock types and 
note exceptions. 
Identifying meteorites amongst terrestrial rocks is the fundamental engineering 
problem of robotic meteorite search and is the topic addressed by the rest of this 
paper. 
2 Bayes network rock and meteorite classifier 
Classifying rocks from a mobile robotic vehicle entails several unique issues: 
The classifier must learn from examples. Human experts often have trouble 
explaining how they can identify many rocks, and will refer to an example. In 
the words of a veteran Antarctic meteorite searcher [2] "First you find a few 
meteorites, then you know what to look for". 
A complication is the difficulty of acquiring large sets of training data, under 
realistic field conditions. To date this has required two earlier expeditions to 
Antarctica, as well as visits to the Arctic and the Atacama desert in Chile. 
Therefore, it is necessary to constrain a classifier as much as possible with 
available prior knowledge, so that training can be accomplished with minimum 
data. 
The classifier must be able to accept incomplete data, and compound evidence 
for different hypotheses as more information becomes available. The robot has 
multiple sensors, and there is a cost associated with using each one. Sensors 
such as the spectrometer are particularly expensive to use because the robot 
must be maneuvered to bring the rock sample into the sensor manipulator 
workspace. Therefore, it is desirable that initial classifications be made using 
data from cheap long range sensors, such as a color camera, before final 
verification using expensive sensors on promising rock samples. 
A corollary of this is that the classifier should accept prior evidence from other 
sources, such as an experts knowledge on what to expect in a particular 
location. 
Rock classes are often ambiguous, and the distinctions between certain types 
fuzzy at best [3]. The classifier must handle this ambiguity, and indicate 
several likely hypotheses if a definite classification cannot be achieved. 
These requirements for a robotic rock classifier argue strongly in favor of a Bayes 
network based approach, which can satisfy them all. The intuitive graphical 
structure of a Bayes network makes it easier to encode physical constraints into the 
network topology, thus reducing the intrinsic dimensionality. Bayesian update is a 
principled way to compound evidence, and prior information is naturally 
represented by prior probabilities. 
Additionally, with a Bayes network it is simple to compute the likelihood of any 
new data, and thus conceivably recognize bad sensor readings. Furthermore, the 
network can be queried to estimate the information gain of further sensor readings, 
enabling active sensor selection. 
2.1 Network architecture 
The (simplified) network architecture for distinguishing rocks from meteorites, 
using features from sensor data, is shown in Figure 3. It is a compromise between a 
fully connected network (no constraints whatsoever, and computationally 
intractable) and a naive Bayes classifier (can be efficiently evaluated, but lacks 
sufficient representational power). Sensor features are only weakly (conditionally) 
dependent on each other because of a careful choice of suitable features, and the 
intermediate node Rock-type, whose states include all possible rock and meteorite 
types likely to be encountered by the classifier. 
A complication is that the sensor features are continuous quantities, yet the Bayes 
network implementation can only handle discrete variables. Therefore the 
continuous variables need to be suitably quantized. 
Rock/Meteorite Meteorite? 
type True 
Iron meteorite False 
Sandstone 
Sensor Sensor Sensor 
feature 1 feature 2 feature N 
Figure 3: Bayes network for discriminating meteorites and rocks based on features 
computed from sensor data. 
2.2 Sensors and feature vectors 
1 
peak 
'Iroug,h _ 
,troug,h 
! i rengtl{ of peak 
(+) or trough (-) at 
given wavelength 
400 600 800 1000 
wavelength/[nm] 
Figure 4: Example spectrum (with extracted features) and color images of rocks 
on ice. One of the rocks in the image is meteorite. 
In Antarctica Nomad acquired reflectance spectra and color images (Figure 4) of 
sample rocks. Spectra are obtained by shining white light on the sample and 
analyzing the reflected light to determine the fraction of light reflected at a series of 
wavelengths. 
The relevant features in a spectrum, for the purpose of identifying rocks, are the 
presence, location and size of peaks and troughs in the spectrum (Figure 4), and the 
average magnitude (albedo) of the spectrum over certain wavelengths. Spectral 
troughs and peaks are detected by computing the correlation of the spectrum with a 
set of 10 templates over a finite region of support (50 nm). Restricting the degree of 
overlap between templates minimizes statistical dependencies between the resulting 
spectral features (Figure 3). Normalizing the correlation coefficients makes them 
(conditionally) independent of the average spectral intensity and robust to changes 
to scale (important, because in practice, when making a field measurement of a 
spectrum it is difficult to accurately determine the scale). A 13 element real valued 
feature vector (each component corresponding to a sensor feature node in Figure 3) 
is thus obtained from the original 1000+ element spectrum. 
Color images are harder to interpret (one of the rocks in Figure 4 is a meteorite). 
First the rock needs to be segmented from the background of snow and ice in the 
image, using a partially observable Markov model [4]. Features of interest are the 
rock cross sectional area (used as a proxy for size, and requiring that the scaling of 
the images be known), average color, and simple texture and shape metrics [4]. 
Meteorites tend to be small and dark compared to terrestrial rocks. An 8 element 
real valued feature vector is computed from each image. 
All real valued features are quantized prior to being entered into the Bayes network, 
which cannot handle continuous quantities. 
2.3 Network training 
The conditional probability matrices (CPM's) describing the probability 
distributions of network sensor feature nodes given Rock type (and other parent 
nodes) are learned from examples (of rock types along with the associated feature 
vectors derived from sensor readings on rock samples of the given type) using the 
algorithm in [5]. If X is a node (with N states) with parent Y, and with CPM Pu = 
P(X=ilY=j), then each column is represented by a Dirichlet distribution (initially 
uniform) and assumed independent of the others. If oq..oN are the Dirichlet 
parameters for P(XlY=j) then  :%/% [6]. Given a new example {X=i,Y=j}with 
weight w the Dirichlet parameters are updated: oq -> oi + w. This is a true Bayesian 
learning algorithm, and is stable. Furthermore, it is possible to weight each training 
sample to reflect its frequency of occurrence for the rock type that generated it. 
This is especially important if multiple sensor readings are taken from a single 
sample 
1 
............  ' ............. .' ............ : ............ :" .............. .' .........      spectrum .... 
i i i i i sensrs I 
o 
false positives 
Figure 5 : Classifier rate of classification curves using laboratory data 
training and testing (25% cross validation), for different sensors. 
for 
The training data (gathered from previous Antarctic expeditions, and from US 
laboratory collections* of meteorites and Antarctic rocks) is insufficient to fully 
populate the (quantized) space on which the CPM's are defined, unless the real 
valued feature nodes are very coarsely quantized. To avoid this, more spectral data 
was generated from each sample spectra by adding random noise (generated by a 
* Johnson Space Center, Houston and Ohio State University, Columbus. 
non-linear spectrometer noise model) to it. (This is analogous to the approach used 
by [7] for training neural networks). 
Using meteorite and terrestrial rock data acquired in the lab, partitioned into 75% 
training, 25% testing cross validation sets, the Rate of Classification (ROC) curves 
in Figure 5 are generated. Note the superior classification with spectra versus 
classification with color images only. In fact, given a spectrum, a color image does 
not improve classification. However, because it is easier to acquire color images 
than spectra, they are still useful as a sensor for preliminary screening. 
3 Antarctica 2000 field results 
In January 2000 the Nomad robot was deployed to the Elephant moraine in 
Antarctica for robotic meteorite searching trials. Nomad searched areas known to 
contain meteorites, autonomously acquiring color images and reflection spectra of 
both native terrestrial rocks and meteorites, and classifying them. On January 22, 
2000 Nomad successfully identified a meteorite amongst terrestrial rocks on the ice 
sheet (http://www. frc.ri.cmu.edu/proj ects/meteorobot2000/). 
0.5 
o 
o 
   , (i) a priori 
 = (ii) retrained 
 (iii) select 
0.5 1 
false positive rate 
Figure 6 : Rate of classification curves for the Nomad robot searching for 
meteorites in Antarctica, 2000 A.D. 
Overall performance (using spectra only, due to a problem that developed with 
camera zoom control) is indicated by the ROC performance curves in Figure 6. 
These were generated from a test set of rocks and meteorites (40 and 4 samples 
respectively, with multiple readings of each) in a particular area of the moraine. 
Figure 60) is using the a priori classifier built from the lab data (used to generate 
Figure 5), acquired prior to arrival in Antarctica. Performance clearly does not 
match that in Figure 5. There is a notable improvement in (ii), the ROC curve for 
the same classifier further trained with field data acquired by the robot in the area 
(from 8 rocks and 2 meteorites not in this test set). 
Even with retraining, classification is systematically bad for a particular class of 
rocks (hydro-thermally altered dolerites and basalts) that occurred in the Elephant 
moraine. These rocks are stained red with iron oxide (rust) whose spectrum has a 
very prominent peak at 900 nm, precisely where many meteorite spectra also have a 
peak. This is not surprising, given that most meteorites contain metallic iron, and 
therefore can have rust on the surface. However, these rocks were absent from the 
initial training set and not initially expected in this area. Performance is much 
better if these rocks are removed from the test set (iii) and the retrained classifier is 
used. 
4 Conclusions 
With the caveat that training be continued using data acquired by the robot in the 
field, the Bayes network approach to robotic rock classification is a viable approach 
to this task. Nomad did autonomously identify several meteorites. However, in 
areas with hydro-thermally altered rocks (iron-oxide stained) the reflection 
spectrometer must be supplemented by other sensors, such as metal detectors, 
magnetometers or more exotic spectrometers (thermal emission or Raman), 
obviously at greater cost. 
Sensor noise and systematic effects due to autonomous robot placement of sensors 
on samples in the unstructured and uncontrolled polar environment are significant. 
They are hard to know a priori and need to be learned from data acquired by the 
robot, and in field conditions, as demonstrated by the significant improvement in 
classification achieved after field retraining. 
Further work needs to be done in selective sensor selection, active modeling of the 
local geographical distribution of rocks, and recognizing bad sensor readings, but 
indications are that this can be done in a principled way with the Bayes network 
classifier and will be addressed in future papers. 
Acknowledgments 
The authors gratefully acknowledge the invaluable assistance of Professor William 
Cassidy of the University of Pittsburgh, Professor Gunter Faure of Ohio State 
University, Marilyn Lindstrom and the staff at the Antarctic meteorite curation 
facility of NASA's Johnson Space Center, and Drs. Martial Hebert and Andrew 
Moore of Carnegie Mellon University. 
This work was funded by NASA, and supported in Antarctica by the National 
Science Foundation's Office of Polar Programs. 
References 
[1] D. Apostolopoulos, M. Wagner, W. Whittaker, "Technology and Field Demonstration 
Results in the Robotic Search for Antarctic Meteorites", Field and Service Robotics 
Conference, Pittsburgh, USA, 1999 
[2] Cassidy, William, University of Pittsburgh Department of Geology, personal 
communication, 1997. 
[3] R. Dietrich and B. Skinner, Rocks and Minerals, Wiley 1979. 
[4] L. Pedersen, D. Apostolopoulos, W. Whittaker, T. Roush, G. Benedix, "Sensing and Data 
Classification for Robotic Meteorite Search", Proceedings of $PIE Photonics East 
Conference, Boston, 1998. 
[5] Spiegelhalter, David J., A. Philip Dawid, St&fen L. Lauritzen and Robert G. Cowell, 
"Bayesian analysis in expert systems" in Statistical Science, 8 (3), p219-283., 1993. 
[6] A. Gelman, J. Carlin, H. Stern, D. Rubin, Bayesian Data Analysis, Chapman & Hall, 
1995. 
[7] D. Pomerleau, "Efficient Training of Artificial Neural Networks for Autonomous 
Navigation", NeurComp vol. 3 no. 1 p 88-97, 1991 
