Wall-Based Registration of Two Scanning Ladar Sensors 

by Gary A. Haas 

ARL-TR-3595 August 2005 

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ARL-TR-3595 August 2005 

Wall-Based Registration of Two Scanning Ladar Sensors 

Gary A. Haas 

Weapons and Materials Research Directorate, ARL 

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This study evaluates the registration between a scanning ladar and a surveying ladar as a possible component in the registration 

of unmanned ground vehicle mobility sensors in the field. The structured scene used to generate registration data consists of 

the concave intersection of two walls and the flat pavement, effectively three orthogonal planes. The registration is formulated 

in terms of the parameters of the three planes. The extraction of the parameters from Cartesian point clouds produced by the 

ladars is described. The registration is generated and evaluated by qualitative techniques. 

15. SUBJECT TERMS

 ladar; registration; UGV

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Contents 

List of Figures iv

1. Introduction 1

2. Registration 1

2.1 Sensors.............................................................................................................................1 

2.2 Composite Registration ...................................................................................................2 

3. Three-Dimensional to Three-Dimensional Registration 4

3.1 Wall-Based Method.........................................................................................................5

3.2 Formulation .....................................................................................................................5 

4. Metrics 7

4.1 Quantitative .....................................................................................................................7 

4.2 Visual Alignment ............................................................................................................8 

4.3 Project Points Into Image ................................................................................................8 

5. This Registration 9

6. Conclusions 12

7. References 13

Appendix A. Intermediate Steps in the Development of Expression for Transformation 

 Between Coordinate Frames 15 

Distribution List 16
iv

List of Figures 

Figure 1. Facets tile to form image ................................................................................................ 2 

Figure 2. Registration scene with red/blue targets and multi-colored checkerboard target........... 4 

Figure 3. The right-hand wall isolated from the SEO data is shown in the top image.................. 6 

Figure 4. SEO data (in red) juxtaposed against Riegl data (in white) (plan view)........................ 8 

Figure 5. Visualization of registration of Riegl sensor data to color camera image at lower 

left ........................................................................................................................................... 9 

Figure 6. SEO data (in aqua) juxtaposed against Riegl data (in black) (plan view).................... 11 

Figure 7. SEO data (red) for large poster juxtaposed with corresponding Riegl data (blue). ..... 11 

Figure 8. The colors assigned to the posters when the SEO data are projected into the camera 

image (right) do not align correctly with the colors and the boundaries of the camera 

image itself (left), indicating a poor registration between SEO sensor and camera ............. 11 
1

1. Introduction 

An autonomous unmanned ground vehicle (UGV) navigates unstructured terrain using sensors to 

detect and to classify its environs. Each sensor detects only a limited number of attributes of the 

world around it. The UGV perception subsystem must fuse the information from multiple 

sensors so that the traversability of terrain elements can be represented to the planning 

subsystem. 

2. Registration 

Integration or fusion of information from multiple sensors requires that the information be 

addressable in a common coordinate system. Sensors of interest to the UGV community, (e.g., 

imaging ladars and color cameras) generate information about the world in the coordinate system 

of the sensor. Although it is possible in principle to mechanically align the coordinate systems, in 

practice, precision alignment is not the rule. Fusion of information from sensors such as these 

requires the data to be converted to a common coordinate system in a process known as 

registration. In the domain of research UGVs, registration between two sensors is typically 

assumed to be time invariant and is performed off line at the time the sensors are installed on the 

UGV and perhaps periodically thereafter. The term “registration” refers to both the process of 

defining the mathematical transformation between sensor coordinate systems and the mathematical transformation itself. 

A registration between a camera and a three-dimensional (3-D) point cloud from a ladar sensor is 

commonly computed from matching features in corresponding imagery produced by the two 

sensors (Elstrom, Smith, & Abidi, 1998). The imagery is frequently from a scene purposely 

designed for the registration, which provides features easily detected in the imagery from either 

sensor. A transformation is computed which consists of the rotation and translation that projects 

all 3-D point features into the corresponding two-dimensional (2-D) image features, minimizing 

some error term. This transformation is the mathematical transformation between the coordinate 

frames of the two sensors and is termed the registration transformation or, more commonly, the 

registration. 

2.1 Sensors 

The sensors of interest in this study are an imaging ladar manufactured by Schwartz ElectroOptics (SEO)1

 and a color video camera. The color camera is generic, producing 2-D color 

 1 Design rights to this unit are now owned by General Dynamics Robotics Systems, Westminster, Maryland.
2

images at 640 by 480 pixels resolution. An 8-mm manual focus lens produces roughly a 45-

degree field of view (FOV). Modeling and calibration are described in Oberle and Haas (2004). 

The SEO ladar unit is rather unique in today’s market in that it generates a 3-D image of its 

environment a number of times per second and so is capable of use on a moving vehicle as a 

geometric obstacle detector in an unstructured world. The 32-row by 180-column range image 

consists of 16 “facets” that comprise 4 rows by 90 columns of discrete elements called “rangels” 

(from “range pixel”). The facets shown in figure 1 tile the field of view of the range image, with 

a few rangels overlapping between corresponding left and right facets. Each rangel represents 

the range to something in the environment which has reflected enough energy to cross the 

detection threshold of the ladar detector. For each rangel, the direction is known in the 

coordinate frame of the sensor, so the magnitude and direction are converted to the more useful 

Cartesian form. The result is a cloud of 3-D Cartesian points in the local sensor coordinate 

frame. The FOV of the unit is 86 degrees horizontal by 20 degrees vertical; its angular 

resolution is 0.658 by 0.5 degree. See Shneier, Chang, Hong, Cheok, and Scott (2003) and 

Hong, Rasmussen, Chang, and Shneier (2002) for other details. 

Figure 1. Facets tile to form image. 

2.2 Composite Registration 

For the data set described herein, the conventional approach to registration (e.g., matching point 

features) was deemed undesirable for the following reasons. First and foremost, point features 

are indistinct in the sparse SEO imagery. The intensity of a rangel return was not available from 

this unit, only the time-of-flight-based range. Thus, there is no reflectance-based image in which 

to look for features apparent in the camera image. 

It is possible to produce a ladar-based image with the use of the two dimensions of the ladar 

“image plane” to define an image point and represent the third dimension as a “false” color. A 

well-silhouetted corner feature in ladar data presented in this fashion can be detected. However, 

the large mismatch in the angular resolution of the two sensors2 limits the ability to localize the 

point correspondence. 

 2

One ladar image point created in this fashion is approximately 50 times the size of a pixel from the camera 

image.

facet
3

Third, the point features most readily distinguished in ladar imagery are well-silhouetted corners. 

However, any edge in a ladar image is subject to the “mixed pixel” effect (Tuley, Vandapel, & 

Hebert, 2004). This occurs when the ladar impinges on objects in the environment at different 

ranges, resulting in returns from each. The range reported is incorrect and is usually intermediate between the two correct ranges. An edge rangel is almost surely a mixed pixel, further 

reducing the certainty of location of the feature. 

A final consideration was an interest in exploring a registration technique requiring less manual 

feature matching, a task not well suited to field operations and maintenance. 

Instead, an approach was implemented, based on a variation of that described in (Shneier et al., 

2003), with elements of the same sensor suite of the Shneier paper. Sensor data from the camera 

and the SEO ladar were separately registered to high-accuracy, high-resolution data from a 

Riegl3 LMS-210 surveying ladar collected from the same registration scene. The registration 

between SEO ladar and camera was to be built from the registrations of the two sensors to the 

Riegl intermediary. The Riegl was used as an intermediate because the resolution is similar to 

that of the camera and because the Riegl produces not only a range at each data point but an 

intensity measure as well. The intensity of the reflectance at each point can be displayed in a 

manner that resembles a camera image, facilitating matching of features between the two 

sensors. 

The structured registration scene shown in figure 2 consists of the concave intersection of two 

walls and the flat asphalt-covered ground. In the foreground are several planar targets on 

stanchions, including one target composed of surfaces and voids alternated in a checkerboardlike configuration. The poster-like targets provide corners evident in both the camera and Riegl 

reflectance images. The scene also contains the ground and two intersecting walls which provide 

planar features evident in data from the SEO and Riegl ladars. The registration between camera 

and Riegl ladar was calculated with conventional camera calibration methods that treated the 3-D 

feature locations from the Riegl sensor as a surrogate for ground truth of the corner features. The 

registration between Riegl and SEO ladars was approached in an unorthodox fashion, which 

comprises the focus of this report. 

 3Manufactured by Riegl, Horn, Austria. 
4

Figure 2. Registration scene with red/blue targets and multi-colored 

checkerboard target. 

3. Three-Dimensional to Three-Dimensional Registration 

Registration between the coarse SEO ladar and the high-resolution Riegl surveying ladar posed a 

new set of issues. The ICP (iterative closest or corresponding point) algorithm, currently the 

dominant method for 3-D-to-3-D registration (Rusinkiewicz & Levoy, 2001), was unfamiliar to 

the author at the onset of the effort, and disappointing early experiences with ICP (not herein 

documented) argued for a simpler approach. Instead, the author implemented a method based on 

the computation of the parameters defining the planes that correspond to the two walls and the 

ground in the registration scene and determined the rotation and translation between the two 

coordinate frames so defined. In general, good descriptions of surfaces can be extracted from 

ladar point clouds because a large number of data points support the small number of parameters 

describing a surface. In such an overdetermined system, the effect of a large error in an 

individual point can be expected to be overwhelmed by the large number of points without large 

error. 

At the desired 10- to 20-meter range (the range of greatest interest in the intended use), the 

resolution of the SEO put only 30 or so points on the 0.5-m-square targets, with the edges subject 

to the “mixed pixel” effect. The corners were not clearly evident in the SEO image. The use of 

planar data from the targets was deemed un-promising. Only the planar data from the walls were 

used in the computation of the registration. 
5

3.1 Wall-Based Method 

The method is to extract from the ground and each of the two walls of the registration scene the 

equation describing the plane embedded therein. The vectors describing the orthogonal planes 

in one ladar’s image are then rotated into the corresponding vectors in the other ladar’s image to 

calculate the rotation and translation between the two images. The process of extracting the 

plane occurs in several steps. First, the SEO point cloud is rotated 180 degrees about the y axis 

to resolve axis-naming conventions between the ladars. The result is an approximate alignment 

between the two sensors. The pair of point clouds is then processed by means of software 

developed at Carnegie Mellon University, described in Vandapel, Donamukkala, and Hebert 

(2003), which separates points on the ground from others in the vegetation above the ground. A 

“ground” point has no points beneath it in the data set, while a “vegetation” (or “non-ground”) 

point occurs above other points. For the flat terrain of the parking lot, the effect is to classify 

the walls and targets (imperfectly) as vegetation. The resulting “ground” is evaluated manually 

for outliers (tops of walls and posters erroneously classified as ground) and these points are 

removed by ad hoc methods. The remaining points are substantially planar, and these are 

submitted to a least squares planar fit, with points that deviate more than a few inches from the 

fit being culled. The remaining points are well described by the calculated plane and are judged 

to belong to the “ground” class. 

The two walls are treated in a similar fashion. The entire point cloud is rotated so that the z axis 

in the new coordinate frame is approximately perpendicular to the surface of one of the walls. The 

rotated wall, which now has the characteristics required of “ground” by the ground extraction 

code, is isolated and extracted. Parameters of the ground plane are calculated and rotated back into 

the original coordinate frame. An example of a wall extracted in this fashion is depicted in 

figure 3. 

3.2 Formulation 

At this point in the process, the parameters of each wall (right, left, and ground4) have been 

calculated in the form 

aws * xws′+ bws = 0 where 

the notation v′ indicates the transpose of vector (or matrix) v;

aws = [a1ws a2ws a3ws], and | aws | = 1; 

a1ws, a2ws, a3ws, and bws are scalar; 

the subscript w can assume the values {1, 2, 3} corresponding to {ground, right wall, left wall}; 

 4

We will hereafter refer to the ground as one of the walls defining the embedded coordinate frame. Hopefully, 

this will not be confusing, especially since we treated the walls as ground in the previous section.
6

the subscript s can assume the values {g, r} corresponding to {SEO or Riegl}; 

xws is of the form [x y z] , 

and x, y, and z are the scalar coordinates of a data point from the w subset of the s sensor point 

cloud in local s sensor coordinate frame. 

Figure 3. The right-hand wall isolated from the SEO data is shown in 

the top image. (Rectangular voids are occluded by posters 

in the foreground, which are eliminated as “non-ground” 

elements of the data. The wall is also shown in white in the 

lower image, rotated 90 degrees to plan view, along with 

points deemed non-ground, shown in blue. Planar targets 

can be seen as clusters of blue points in lower right of this 

image.) 

Let us use these results to determine the rotation and translation between the coordinate frames 

of the two sensors. 

First, we interpret the wall parameters. The unit vectors a1s, a2s, and a3s are the perpendicular 

vectors to the three walls in the coordinate system of sensor s. They form the basis of a 

coordinate frame embedded in the walls of the scene, as seen by the sensor s. The origin of this 

embedded coordinate frame is the intersection of the three walls. We call the embedded 

coordinate frame “e.” These basis vectors can be organized into a rotation matrix. 

The rotation matrix As, defined as 
7

A

a

a

a

s

=

L

N

M

M

M

O

Q

P

P

P

′

1

2

3

s

s

s

 for s = { g , r }, 

rotates a point in the embedded coordinate frame to the coordinate frame of the s sensor. 

The vector bs = [b1s b2s b3s]′ is the origin of the sensor frame, measured in the embedded 

frame, and so represents the translation from the sensor coordinate frame to the embedded 

coordinate frame. A point pe in the embedded coordinate frame can be mapped to the 

coordinate frame of sensor s by 

ps = As * pe - (As * bs ). (1) 

Now we compute the rotation Rrg and translation trg to transform a point in the coordinate 

frame of the g sensor to the coordinate frame of the r sensor. 

The rotation Rrg between the coordinate frames of the two sensors can be calculated as the 

rotation between the orthogonal basis matrices Ag and Ar, 

Rrg = Ar * inv(Ag) .

The translation between the sensor coordinate frames is somewhat more complex because the 

measured translations are defined in the embedded frame, not in either sensor frame. The 

translation between the coordinate frames of the two sensors is expressed as 

trg = (Ar * ( bg - br )).

Intermediate steps in the development of this expression are documented in appendix A. 

4. Metrics 

4.1 Quantitative 

Although there are several alternate ways of calculating the transformation between coordinate 

frames, there is no definitive quantitative measure of the correctness of the transformation. 

Rusinkiewicz and Levoy (2001) suggest a metric based on the sum of squared distances (SSD) 

from a point to the nearest surface. In this case, this could be implemented as the SSD from SEO 
8

points to the corresponding Riegl plane. This measure was not implemented as part of this study 

but should be considered for any successor studies. 

4.2 Visual Alignment 

Qualitative measures are commonly used to assess the correctness of the transformation. One 

measure is inspection of the alignment of one point cloud transformed into the coordinate frame 

of the other. The mechanism used in this case is the Virtual Reality Modeling Language 

(VRML) visualization tool.5 The SEO points were transformed to the Riegl frame with the 

registration calculated as described previously and were assigned a color (red). The Riegl points 

were assigned a different color (white), and both sets of points were viewed as part of the same 

VRML view (see figure 4). With built-in tools for moving the viewpoint around in the viewer, 

gross misalignment of the two point sets is apparent. Minor misalignments may escape scrutiny, 

however. One disadvantage is that the viewer has no cue to differentiate points in the foreground 

from points in the background, so features that cannot easily be silhouetted are difficult to assess. 

Another disadvantage is that the only depth cues come from moving the viewing perspective, so 

documenting phenomena for a presentation or paper is difficult. This method was used for initial 

assessment of the quality of 3-D-to-3-D registration. 

Figure 4. SEO data (in red) juxtaposed against Riegl 

data (in white) (plan view). (Note that the 

SEO FOV is wider than Riegl FOV.) 

4.3 Project Points Into Image 

A method for evaluating the quality of a 3-D-to-2-D registration is to project the 3-D points into 

the corresponding image6. A 3-D point is assigned the color of the pixel into which it projects 

 5

I am indebted to Dr. Nicolas Vandapel for introducing me to this approach.

6I am indebted to Dr. Vandapel and Mr. Ranjith Unnakrishnan for suggesting this method.
9

and is viewed in a color VRML view. A high-quality registration is distinguished by appropriate 

color of the 3-D features in the view. This method was used to evaluate the end-to-end 

registration between camera and one or the other of the ladar sensors. It works reasonably well 

for high-resolution point clouds such as the Riegl. The low-resolution point clouds of the SEO 

were more difficult to assess. When the camera and ladar are far apart, points in the background 

frequently assume unexpected colors, since the sensors are occluded differently by features in the 

foreground. Only features in the foreground can be relied upon to display the color expected. 

An example of a good registration is shown in figure 5, which depicts the points from the Riegl 

ladar projected into the color camera image. 

Figure 5. Visualization of registration of Riegl sensor data to color 

camera image at lower left. (The colors of the 3-D foreground 

posters match the image, as do un-occluded elements of the 

background [the door, for example], indicating a good 

registration.) 

5. This Registration 

The registration data were collected in May 2003 at the campus of the National Institute for 

Standards and Technology in Gaithersburg, Maryland. 

The rotation between Riegl and SEO sensors was computed with the technique described herein. 

The equations describing the walls, as seen by the SEO sensor, were 
10

Ar′ = bs = 

 0.098932 0.0089963 0.99505 2.2564 

 0.83984 -0.53779 -0.073874 12.23 

 0.53257 0.84365 -0.06791 15.453 

Ag′ = bs = 

 0.099795 0.0049212 0.995 0.74841 

 0.83659 -0.54593 -0.045581 13.288 

 0.55443 0.83099 -0.045497 16.101 

Note that in this form, the rows of the matrices are in the form of aws. Since these As matrices 

are not quite orthogonal, they are brought into orthogonality by singular value decomposition (svd). 

Next, the rotation between the sensor coordinate frames is computed. The rotation from the SEO 

coordinate frame to the Riegl coordinate frame is computed as 

Rrg = 

 0.99969 -0.017033 0.017899 

 0.016979 0.99985 0.0031528 

 -0.01795 -0.002848 0.99983 

Finally, the translation from SEO to Riegl coordinate frames is computed: 

trg = 

 1.0851 

 -0.042551 

 -1.6228 

The registration is evaluated first by an inspection of the alignment juxtaposing the registered data 

sets in a 3-D visualization (see figure 6). The alignment of the walls is substantially better than in 

figure 4. However, close inspection reveals that the alignment of the posters is poor (figure 7). 

The registration is then evaluated by projection of the SEO points into the camera image, as was 

done in figure 3. The results, shown in figure 8, corroborate that the SEO sensor and camera are 

not well registered. In particular, there appears to be an offset of approximately 1 foot in the 

vertical direction. There is also an apparent discontinuity in the SEO data where the right and 

left facets merge. The calculated rotation between the two sensors appears correct. 

Other anomalies were identified in the vicinity of the red and blue targets, which were constructed 

of retro-reflective material. The SEO sensor appears to detect these targets at a relatively shorter 

range than does the Riegl. The large multi-colored target, which is not retro-reflective, does not 

evidence this effect. 
11

Figure 6. SEO data (in aqua) juxtaposed against 

Riegl data (in black) (plan view). 

Figure 7. SEO data (red) for large poster juxtaposed 

with corresponding Riegl data (blue). 

Figure 8. The colors assigned to the posters when the SEO data are projected into the camera image (right) 

do not align correctly with the colors and the boundaries of the camera image itself (left), 

indicating a poor registration between SEO sensor and camera. 
12

6. Conclusions 

The wall-based approach to aligning two imaging ladar sensors is an appealing notion. Upon 

sufficient reflection, the geometry is intuitive, and the mathematics to implement it are straightforward. 

The benefit of such an approach is that walls in a mutually orthogonal configuration are available 

almost anywhere, so in principle, the ladar-to-wall portion of a UGV sensor alignment could be 

improvised in the field with a minimum of equipment and with relatively unsophisticated software. 

The wall-to-camera portion of the registration is required to complete the ladar-to-camera registration, which is the end-to-end result desired. Wall-to-camera registration without manual feature 

designation in such an improvised registration scene is left to another study. 

However, this study did not result in a successful registration of the two sensors with this data 

set. The most likely source of error is the calibration of the SEO sensor used to convert the range 

image to a Cartesian point cloud. The calibration used in this study is based on a theoretical 

(rather than calibrated) set of direction vectors mapping the range image into a cloud of 3-D 

points. At least at the center where the two facets overlap, the calibration is suspect. A precise 

determination of the source of error is beyond the scope of this study. 

The visualization tools used to evaluate the quality of the registration were inadequate, especially 

given the resolution of the SEO sensor. Having more than one (preferably at least three) corner 

point in the FOV would help quantify the effectiveness of the registration. It would also be 

helpful to increase the density of the SEO scan by slewing the sensor, although it increases the 

complexity of the sensor calibration. 
13

7. References 

Elstrom, M.; Smith, P.; Abidi, M. Stereo-based registration of LADAR and color imagery, SPIE 

Conference on Intelligent Robots and Computer Vision, 1998, pp. 343-354. 

Hong, T.; Rasmussen, C.; Chang, T.; Shneier, M. Fusing Ladar and Color Image Information for 

Mobile Robot Feature Detection and Tracking. Proceedings of the 7th International 

Conference on Intelligent Autonomous Systems, Marina del Rey, CA, March 2002. 

Oberle, W.; Haas, G. LADAR-Camera Registration Using Object Pose and Camera Calibration 

Algorithms ; ARL-TR-3147; U.S. Army Research Laboratory: Aberdeen Proving Ground, 

MD, April 2004. 

Rusinkiewicz, S.; Levoy, M. Efficient Variants of the ICP Algorithm, Third International 

Conference on 3-D Digital Imaging and Modeling (3DIM ‘01), Quebec City, Canada, May 

2001, p. 145. 

Shneier, M.; Chang, T.; Hong, T.; Cheok, G.; Scott, H.S. A Repository of Sensor Data for 

Autonomous Driving Research, Proceedings of the SPIE Aerosense Conference. Orlando, 

FL, 2003. 

Tuley, J.; Vandapel, N.; Hebert, M. Analysis and Removal of Artifacts in 3-D LADAR Data, 

IEEE International Conference on Robotics and Automation, 2005. 

Vandapel, N.; Donamukkala, R.R.; Hebert, M. Quality Assessment of Traversability Maps From 

Aerial LIDAR Data for an Unmanned Ground Vehicle, International Conference on 

Intelligent Robots and Systems (IROS), October 2003. 
14

INTENTIONALLY LEFT BLANK
15

Appendix A. Intermediate Steps in the Development of Expression for 

Transformation Between Coordinate Frames 

From equation 1, for some sensor s, a point pe in the embedded coordinate frame can be mapped 

to the coordinate frame of sensor s by 

ps = As * pe - (As * bs ). (1) 

Conversely, 

pe = inv(As) * ps + bs 

Recall that bs is a measure in the embedded frame e.

The point in coordinate frame s can be mapped to another coordinate frame t by 

pt = At * pe - (At * bt ) 

 = At * (inv(As) * ps + bs ) - (At * bt ) 

 = At * inv(As) * ps + At * bs - (At * bt ) 

 = Rts * ps + (At * ( bs - bt )) 

 = Rts * ps + tts

So the translation tts from frame s to frame t is 

tts = (At * ( bs - bt ))

and the rotation Rts from frame s to frame t is 

Rts = At * inv(As) .
16

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 12350 RSCH PKWAY 

 ORLANDO FL 32826-3726 

 1 CDR US ARMY TRADOC 

 BATTLE LAB INTEGRATION & TECH DIR 

 ATTN ATCD B J A KLEVECZ 

 FT MONROE VA 23651-5850 

 1 OFC OF THE PROJECT MGR 

 MANEUVER AMMUNITION SYSTEMS 

 ATTN S BARRIERES 

 BLDG 354 

 PICATINNY ARSENAL NJ 07806-5000 

 1 CDR US ARMY TRADOC ANALYSIS CTR 

 ATTN ATRC WBA J GALLOWAY 

 WHITE SANDS MISSILE RANGE NM 88002 

 1 CDR USAAMC 

 DEPUTY G3 CURRENT OPERATIONS 

 ATTN N BIAMON 

 5001 EISENHOWER AVE 

 ALEXANDRIA VA 22333-0001 

 2 CDR US TACOM-ARDEC 

 ATTN AMSTA AR TD J HEDDERICK 

 B MADECK 

 PICATINNY ARSENAL NJ 07806-5000 

 2 CDR US TACOM-ARDEC 

 ATTN AMSTA AR TD J HEDDERICK 

 B MADECK 

 PICATINNY ARSENAL NJ 07806-5000 

 1 CDR USAARDEC 

 ATTN AMSRD AAR AE COL P JANKER 

 BLDG 94 

 PICATINNY, NJ 07806-5000 
17

NO. OF 

COPIES ORGANIZATION

 1 PEO SOLDIER 

 ATTN C TAMEZ 

 5901 PUTNAM ROAD 

 BLDG 328 

 FT BELVOIR VA 22060-5422 

 ABERDEEN PROVING GROUND

 1 DIRECTOR 

 US ARMY RSCH LABORATORY 

 ATTN AMSRD ARL CI OK (TECH LIB) 

 BLDG 4600 

 2 CDR US ARMY TECOM 

 ATTN AMSTE CD B SIMMONS 

 AMSTE CD M R COZBY 

 RYAN BLDG 

 4 DIR US AMSAA 

 ATTN AMXSY D D SHAEFFER/ 

 W BROOKS 

 AMXSY CA G DRAKE/S FRANKLIN 

 APG MD 21005-5067 

 1 CDR US ATC 

 ATTN CSTE AEC COL BROWN 

 BLDG 400 

 2 DIRECTOR 

 US ARMY RSCH LABORATORY 

 ATTN AMSRD ARL WM J SMITH 

 T ROSENBERGER 

 BLDG 4600 

 1 DIRECTOR 

 US ARMY RSCH LABORATORY 

 ATTN AMSRD ARL WM B J MORRIS 

 BLDG 4600 

 2 DIRECTOR 

 US ARMY RSCH LABORATORY 

 ATTN AMSRD ARL WM BA D LYONS 

 AMSRD ARL WM BD B FORCH 

 BLDG 4600 

 1 DIRECTOR 

 US ARMY RSCH LABORATORY 

 ATTN AMSRD ARL WM BC P PLOSTINS 

 BLDG 390 

NO. OF 

COPIES ORGANIZATION

 7 DIRECTOR 

 US ARMY RSCH LABORATORY 

 ATTN AMSRD ARL WM BF S WILKERSON/ 

 R ANDERSON/P BUTLER/ 

 C PATTERSON/J WALL/ 

 M BARANOSKI/W OBERLE 

 BLDG 390 

 4 DIRECTOR 

 US ARMY RSCH LABORATORY 

 ATTN AMSRD ARL WM MB DOWDING 

 AMSRD ARL WM B VANLANDINGHAM 

 AMSRD ARL WM MC M MAHER 

 AMSRD ARL WM MD W ROY 

 AMSRD ARL WM MA S MCKNIGHT 

 BLDG 4600 

 3 DIRECTOR 

 US ARMY RSCH LABORATORY 

 ATTN AMSRD ARL WM T P BAKER 

 AMSRD ARL WM TC R COATES 

 AMSRD ARL WM TB R SKAGGS 

 BLDG 309 

 1 DIRECTOR 

 US ARMY RSCH LABORATORY 

 ATTN AMSRD ARL WM TD SCHOENFELD 

 BLDG 4600 

 1 DIRECTOR 

 US ARMY RSCH LABORATORY 

 ATTN AMSRD ARL WM TE B RINGERS 

 BLDG 1116A 

 1 DIRECTOR 

 US ARMY RSCH LABORATORY 

 ATTN AMSRD ARL WM T M ZOLTOSKI 

 BLDG 393