1

Obstacle Detection and Mapping System

Tsai-Hong Hong, Steven Legowik, and Marilyn Nashman

Intelligent Systems Division, National Institute of Standards and Technology (NIST)

Bldg. 220, Rm. B124, Gaithersburg, MD 20899
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1. Introduction

The ability to detect and avoid obstacles is a prerequisite for the success of the Demo 

Unmanned Ground Vehicles (UGV) program. The near term goals of the project are to 

able to drive the High Mobility Multipurpose Wheeled Vehicle (HMMW

autonomously on cross-country roads with the ability to:

1. Drive autonomously at speeds up to 10 m.p.h., controlled as appropriate for 

terrain and vehicle dynamics.

2. Detect obstacles and rough terrain conditions in time to enable deceleration to a s

speed or the ability to steer around the problem condition.

3. Maintain a control station with an interface for limited mission planning and d

collection.

This paper discusses an obstacle detection algorithm developed at NIST in support of 

obstacle detection and rough terrain conditions. The algorithm is a hybrid of grid-ba

and sensor-based obstacle detection and mapping techniques. The perception and obsta

detection/mapping module is part of the integrated 4D-Realtime Control System (RC

system [1][2]. It consists of two sections: an obstacle detection section and a mapp

section. The obstacle detection section processes range data read from a Ladar sen

The algorithm converts range data into Cartesian coordinates in the sensor coordin

frame, and uses this information to detect obstacles. The second section, the mapp

module, projects obstacle points onto a grid-based map. The map is used by the 4D-R

planner module [11] to generate a traversable path for the vehicle.We have demonstra

autonomous driving with obstacle detection and avoidance on the NIST grounds and 

Nike site at speeds of up to 24 km/h.
 3

Section 2 describes the sensors used in the 4D-RCS autonomous driving system. Sect

3 describes in more detail the Ladar sensor’s characteristics. Section 4 describes 

obstacle detection algorithm. Section 5 evaluates the algorithm performance on b

artificial and natural obstacles. Section 6 briefly describes the obstacle mapping modu

In Section 7, we present our conclusions and plans for future work.

2. System Sensors

The sensors used in the system include a Dornier 1.2 Hz. Ladar Range Imaging Cam

(EBK); the Ashtech Z12, a Global Positioning System (GPS) sensor, and an Iner

Navigation System (INS) sensor [14]. The inertial sensor is the U.S. Army’s Modu

Azimuth and Positioning System (MAPS) which contains three ring laser gyros, th

accelerometers, and a rear axle odometer. All sensors are mounted on the HMMW

shown in Figure 1. The vehicle is equipped with electric actuators added to the steeri

brake, transmission, transfer case, and parking brake. Dashboard-type feedback provi

the controller with RPM, speed, temperature, fuel level, etc. Multiple navigation sens

are used. A Kalman filter [9] computes vehicle position and orientation using data fr

an inertial dead reckoning system and a carrier phase differential GPS unit,1

1. Certain commercial equipment, instruments, or materials are identified in this paper in order to

adequately specify the experimental procedure. Such identification does not imply recommendation or endorsement by NIST, nor does it imply that the materials or equipment identified are

necessarily best for the purpose.
 4

3. The EBK Ladar sensor

We are using the EBK Ladar sensor as the primary obstacle detection sensor 

autonomous driving. Table 1 shows a summary of the characteristic parameters 

Dornier’s 1.2 Hz Ladar camera. The vertical pixel spacing is mechanically fixed at 

degrees by the spacing of the fiber optic array. This gives a vertical field of view of 3

degrees. The exact horizontal field of view and pixel spacing may vary slightly beca

the horizontal and vertical scans are not mechanically linked. However, they can 

calculated exactly from scan mirror position data encoded in the image. The nom

value for the horizontal field of view is 60 degrees. In addition, there may be locali

variations of the horizontal angular position of the image pixels due to mechan

disturbances of the sensor, but these can be detected and accounted for in ra

computations. The range image is generated by scanning a laser beam over the field 

view. At each point in the scan, a pulse of laser light is emitted. This pulse is reflec

back to the sensor from the object in its line of flight. The range of the object can 

calculated from the light pulse’s time of flight. Because the measurement uses time 

flight rather than phase detection, the sensor produces an absolute value with no ra

ambiguity. Due to the short duration of the laser pulse, the EBK is eye-safe.

Figure 1. NIST HMMWV
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The laser is scanned horizontally across the field of view by an oscillating mirror. T

beam is scanned vertically by a nutating mirror which reflects the beam through a circu

fiber optic array. This fiber optic array converts the circular motion of the nutating mi

into a linear scan in the image plane. Additional technical information about the EBK 

be found in [3][4].

3.1 Effects of Different Materials on Range Data

We have examined the sensor’s ability to measure different materials in order to determ

its sensitivity. In order for the sensor to receive a laser pulse reflection, the reflection m

be diffuse, i.e., the reflected laser light must travel in all directions from the target, one 

which is back to the sensor. A highly specular surface may produce a false range readi

3.1.1 Absorptive Materials

A false detection can occur when the surface of an object absorbs so much of the incid

light that the returned light pulse does not have enough energy to trigger the recei

Examples of absorptive materials are black clothing, dark hair, painted black stripes, 

black baseboard along a wall, and very dark tar stains on asphalt. The sensor does 

receive a reflected signal from these objects within the time of flight window, which is 

for a 50 m maximum range. When no reflected light pulse is detected, the invalid tim

results in an invalid range value. Some black colored objects, however, do show 

Automobile tires and black pavement return sufficient light to trigger the detector. 

object’s visibility depends on its reflective properties at 910 nm. In practice, m

surfaces in natural and artificial scenes offer a return signal of sufficient intensity 

calculate a range value.
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3.1.2 Specular Materials

Highly reflective objects give rise to specular reflections. A good example of this is a la

which the sensor cannot detect. Wet pavement also does not register well with the sen

When the Ladar scans a shiny cylindrical metallic object, only the surface of the cylin

whose normal vector is parallel with the sensor’s scanning vector produces a good ra

reading. All other laser pulses are reflected away from the scanner. However, th

reflections may bounce off of other objects and eventually return to the sen

Consequently, ranges to reflective surfaces may appear greater than their true value.

Table 1 EBK operational parameters

EBK Characteristic Parameters

Laser Wavelength 910 nm

Optical peak power 10 W

Imaging Range 5m to 50 m (typical)

Range Resolution 0.06 m

Frame Rate 1 Hz

Field of View 31 degrees by 60 degrees

Beam Divergence 0.2 degrees

Beam Spacing 0.5 degrees

Eye safe class 1, IEC 825 and DIN VDE 0837

Scanning Vertical: fiber optics

Horizontal: oscillating mirror

Operating Voltage 24 VDC (18 V - 28 V)

Power Consumption 110 W

Data Interface RS422 serial, 10 MBit/sec

Weight 15 kg

Dimensions 30 cm x 28 cm x 27 cm

Operating Environment IP65, C to C –100 500
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In nature, the primary source of specular reflections is water. Free standing water, such 

lakes and puddles, almost completely reflect the laser beam from the sensor. In th

cases, no return from the water’s surface is detected. This appears as a “no return val

in a range images The cause of this failure is either the sensor seeing a reflection of 

sky (out of range) or the water absorbing the incident beam.

The signal returned from wet surfaces varies with distance and incident angle. W

pavement tends to disappear at close range and far away where the incident angle of 

sensor beam is small. However, a good signal is returned in the mid range of 25 m.

The EBK can measure range readings from ice and snow. However, Dornier has repor

that partially thawed and refrozen snow produces a less favorable result.

3.1.3 Vegetation

Vegetation poses the greatest problem to obstacle detection using range data. Thi

because range data gives very little information about the solidity of an object or 

traversability. This is especially a problem for off road driving. In addition, vegetat

can obscure other vehicle hazards, such as rocks or ditches hidden in tall grass, making 

hazards more difficult to detect.

3.1.4 Fog

The 910 nm wave length of the laser used by the EBK falls in the near infrared portion 

the electromagnetic spectrum. It was chosen by Dornier for its good reflective propert

Being close to the visible spectrum, the laser is affected by fog. The back scattering ef

caused by heavy fog causes a range value of approximately 5 m to be returned by 

sensor.
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4. Obstacle Detection Module

The purpose of the obstacle detection module is to extract areas which cannot or should not 

traversed by the HMMWV. Rocks, fences, trees, and steep slopes are examples of obstacle are

Many approaches for extracting obstacles from range images have been proposed The m

common approach is to fit a plane surface to a patch of points [6][8][16] in a local g

representation. For example, Hebert [6] and Kelly [10] have used the approach of fitting pla

surfaces to a patch of points to detect obstacles. Their algorithms are simple and fast but are 

very robust: small sensor position errors result in false indications of obstacles. We prop

using differential depth and slope in sensor space in an improved, fast obstacle detector. T

algorithm was inspired by Veatch and Davis [15] and Lux and Schaefer[12]. The details of 

algorithm and an analysis of its robustness using actual outdoor data are given in the follow

subsections.

4.1 The Obstacle Detection Algorithm

The data acquisition time for a complete Ladar image is 0.82 seconds. The image consists of 

vertical scan lines, each containing 64 range values. The sensor geometry was described 

Section 3. Approximately half of the acquisition time is needed to read 8192 range values. T

remaining time is needed for the back-scan. We process range data as they are read, rather t

waiting for a full image. In this way, we can detect an obstacle as soon as the data are availabl

As each scan line is read, a filter is applied to the data. The filter removes outlier points caused 

specular reflections, e.g. water, or reflections within the sensor. After the data are filtered, 

Cartesian (x, y, z) location of each pixel in the scan line is computed in a coordinate sys

centered in the EBK sensor. Each pixel contains x, y, z and range(r) values which are used 

compute depth derivatives and surface slopes. The criteria used for detecting obstacles are:
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1. A discontinuity in elevation exceeding some value T

2. The surface slope exceeding some value, α2

.

These conditions are expressed mathematically in the following way:

Let p(i, j), be the ith position pixel in the jth scan line of the EBK image.

Let x(i), y(i), z(i) be the Cartesian coordinates of this pixel.

Let r(i) be the range value at this pixel.

For every p((k, j), x(k), y(k), z(k), and r(k)) where (i-n) <= k <= (i+n)

(n = 2 in our experiments)

p((k, j), x(k), y(k), z(k), r(k)) is voted an obstacle if:

To avoid frequent false positive detections, a local neighborhood of each pixel votes on whethe

not a pixel is an obstacle. In practice, a neighborhood of five pixels is used. Each pixel in the s

line can receive up to 2n votes where n is the neighborhood size. The higher a pixel’s vote, 

higher the confidence that the pixel is an obstacle. If the confidence is greater than some thresh

value, the pixel is labelled as an obstacle. We empirically find a confidence threshold value o

for our experiments.

5. Analysis and Evaluation of the Obstacle Detection Algorithm

Two types of tests were performed to analyze and evaluate the obstacle detection algorithm. B

scenarios involved collecting sets of Ladar data which were analyzed at a later time. The first 

of tests was performed at the NIST Nike Test Site in Gaithersburg, Maryland. The obstac

z i 〈 〉 – z k〈 〉 ≥ T

〈 〉 z i 〈 〉 – z k〈 〉 2 〈 〉 x i 〈 〉 – x k〈 〉 2 〈 〉 y i 〈 〉 – y k〈 〉 2 〈 〉 z i 〈 〉 – z k〈 〉 2 ( )α ⁄ [ ] + +

2 ≥

and
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tested were artificial, i.e., known size blocks of wood arranged in different groupings. T

second set of tests was performed at the U. S. Army Aberdeen Proving Grounds (APG

Aberdeen, Maryland. This set of data was collected at a surveyed site that included ro

and ditches.

5.1 Artificial Obstacles

Our objective at the Nike site was to evaluate the algorithm’s ability to detect obstacle

known sizes and to determine the maximum range at which different size obstacles co

be detected. The obstacles consisted of sections of 61 cm x 15 cm lumber pre-cut into 

cm, 15 cm and 20 cm sections. These blocks were grouped together to form obsta

groups of varying, but known, dimensions. The obstacles were placed on a road (Fig

2), and Ladar data were collected as the HMMWV approached them. Figures 3, 4 an

are sets of processed Ladar images showing the results of the obstacle detect

algorithm on groups of different size blocks. The blocks in Figure 3 are 20 cm wide by 

cm high. The blocks in Fig. 4 are 30 cm wide by 20 cm high. In Fig. 5, the blocks 

25 cm wide by 15 cm high. The left image in each set is the farthest range at which 

Figure 2 Obstacle blocks at Nike site
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obstacles were detected; the right image is the same set of obstacles at a closer range. In Fig

only one block is detected at 17.5 m while all blocks are detected at 9.5 m. In Fig. 4, 2 blocks 

detected at 22 m, and 4 blocks are detected at 13.2 m. In Figure 5, 3 blocks are detected at 19 

and 4 blocks are detected at 15.7 m.

Because of the sensor design (see Section 3), the probability of the laser beam hitting a target 

function of both the size of the target and its range from the sensor. This relationship is shown 

the graphs plotted in Figure 6. In each of the plots, the horizontal axis measures range in me

from the sensor. The vertical axis is the probability that the Ladar beam will hit the target. 

Figure 6a, the probability of the beam hitting the target increases to 100 % at a distance of 27.5 

and remains at this level through the remaining valid data range. In Figure 6b, the probabi

increases to 100 % at 36 m and remains at 100 % for ranges less than 36 m. The same is true 

Figure 6c for ranges between 5 m and 47.8 m. The target sizes in Fig. 6a, 6b,and 6c are 15 cm 

15 cm, 20 cm x 20 cm, and 25 cm x 25 cm respectively. In addition to the blocks of wood, gr

and trees along the side of the road are also detected as obstacle pixels.
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.

Figure 3 Target Size: 15 cm x 20 cm(width x height)

Obstacle detection at 17.5 m Obstacle detection at 9.5 m

Obstacle detection at 22 m

Figure 4 Target Size 30 cm x 20 cm

Obstacle detection at 13.2 m

Obstacle detection at 19 m Obstacle detection at 15.7 m

Figure 5 Target Size: 25 cm x 15 cm
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5.2 Natural Obstacles

In this section, we describe our analysis of natural obstacles. Our objective was to determine 

farthest range at which pixels representing depressions could be detected. The data collect

site was the Perryman site, located in the U. S. Army Aberdeen Proving Grounds in Aberde

Maryland. In addition to the Ladar data, sets of images from 3 pairs of stereo cameras w

also collected.2 The stereo data were analyzed by scientists at NASA’s Jet Propuls

Laboratory (JPL) using algorithms designed for stereo inputs [13]. This report discusses o

2. A complete description of the data collection can be found at:

http://isd.cme.nist.gov/staff/coombs/proj/mobility/apg-data/doc/datanotes.html.

Figure 6: Probability plots of ladar beam hitting target.

(a) Target size 15 cm x 15 cm (b) Target size 20 cm x 20 cm (c) Target size 25 cm x 25 cm

(a)

(b)

(c) .
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the results of the Ladar obstacle detection algorithm.

Figure 7 is a map of the sections of Perryman in which negative obstacle (ditch) data w

collected. The ditches were dug by a back hoe. Ditch 1 is 0.61 m wide; Ditch 2 is 1.2

wide, Ditch 3 is 1.83 m wide, and Ditch 4 is 2.44 m wide. The lengths of all the ditches 

the same. This data was used to evaluate the effectiveness of the algorithm in detect

negative obstacles (depressions in the ground plane). Figure 8 is a black and white ima

digitized from video tape, showing the site referred to in the ditch survey map. T

viewing direction in Figure 8 is along the direction of the approach vector in Figure 

Data were collected at different times of day under varying weather conditions. 

decrease in daylight did not affect the Ladar data which performed at the same le

Figure 7 Ditch Survey Site

t 1.83 m 2.44 m

Figure 8Ditches at Perryman

1.22 m
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during dawn, daylight, and evening hours. For each of the ditches, the HMMWV w

driven at speeds of 8 kilometers per hour (km/h), 16 km/h, and 24 km/h The vehic

approach to the ditches also varied: some runs were perpendicular to the ditches; so

were oblique. The output of the obstacle detection algorithm was qualitatively evaluat

Grouping of obstacle pixels into objects is planned at a future time and was not a facto

this evaluation. Figure 9 is a set of images showing the unprocessed Ladar data (Fig

9a) and the processed data (Figure 9b) of a sample image taken from the “ditch 1" d

set.The raw data is encoded as grey scale values such that darker grey values repres

ranges farther from the sensor. White pixels in the processed image represent pix

labelled as obstacle points. Although it is very difficult to recognize the ditch in the r

image, the ditch pixels are detected in the processed image The ditch is 10 m from 

vehicle in Figure 9.

Figure 10 is another example of negative obstacle detection. This image set shows r

and processed data from the “ditch 4" data sequence. Again, the pixels associated with 

ditches are extracted. In this figure, the ditch is 15 m from the vehicle.

Table 2 shows the results of the obstacle detection algorithm on the ditch data as 

vehicle approached the ditches in the normal direction. The distances, measured 

meters, represent the farthest distance at which the algorithm extracted pixels represent

Figure 9 Ditch 1: Raw and processed ladar data

(a) (b)
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ditch data. The pixels associated with the ditch are qualitatively recognizable to a traine

eye.

As an aid to understanding these values, it is helpful to understand that the minimum

stopping distance required for a vehicle travelling at 5 m.p.h. (assuming a relatively fl

ground surface) is approximately 10 meters. At 10 m.p.h., the stopping distance is 2

meters, and at 15 m.p.h., it is 30 meters.

Figure 11 is a map of the area of Perryman containing the rock obstacles. The rocks wer

placed both on-road and off-road. The rocks labelled “rock 1” and “rock 2” ar

approximately 15 cm high and 25 cm wide. Rocks 3 and 4 are approximately 30 cm hig

and 30 cm wide. Rock 5 is approximately 45 cm high and 30 cm wide. The rock cluster 

(a) (b)

Figure 10 Ditch 4: Raw and processed ladar data

Table 2 NEGATIVE OBSTACLE DETECTION- NORMAL

APPROACH

Ditch 1 Ditch 2 Ditch 3 Ditch 4

 5 mph. 10.0 m 19.0 m 17.0 m 23.0 m

 10 mph. 10.0 m 14.0 m 20.0 m 20.7 m

 15 mph. 9.5 m 15.0 m 20.5 m 21.2 m
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approximately 45 cm high and 100 cm wide.

Figure 11 Perryman Survey of

Rock Obstacles

Direction of approach

East(m)
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 Figure 12 is a video image showing rocks 1, 2, 3 and 4.

Figure 13 is a magnified video image showing the rock cluster.

Figure 14a shows a Ladar scene of rocks 1 and 2 and Figure 14b the obstacles detected in

this image. Rocks 1 and 2 are highlighted by white boxes in the right image.

Figure 12 Rocks 1, 2, 3 and 4

Time stamp encoded bar

Figure 13 Rock Cluster at Perryman

Figure 14 (a) Raw ladar image of rocks 1 and 2

(a)Raw ladar data (b) Rock 1 at a distance of 11.5 m

 Rock 2 at a distance of 19.2 m
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Figure 15 shows raw (Fig. 15a) and processed (Fig. 15b) Ladar image data for rocks 3 an

4 with boxes highlighting the rocks.

Figure 16a is a Ladar scene of the rock cluster. Figure 16b shows the extracted obstac

points. The direction of approach for Figs. 14, 15, and 16 is from Northeast to Southwe

in Fig. 11. Table 3 is a summary of the results of applying the obstacle detectio

algorithm to the data sets collected at different times of day. The variability of detection 

a result of the following factors:

(1) Data acquisition rate (1 image per second).

(2) Relationship between vehicle velocity and the time an image is acquired. If th

vehicle is moving quickly, the scanning Ladar beam may miss the obstacle completel

(3) Difficulty in identifying the targeted obstacle when it is hidden in tall grass.

(a) Raw ladar data (b) Obstacle points

Figure 15 Rocks 3 and 4

(a)Raw ladar data (b) Rock 3 at a distance of 18.4 m

 Rock 4 at a distance of 25.0 m

Figure 16 Rock Cluster

(a) Raw ladar data (b) Rock cluster at a distance of 25 m
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The need for recognizing and classifying obstacles is clear from the outputs shown i

these figures. In addition to the rocks and/or rock cluster, the algorithm has flagged ta

grass, bushes, and background trees as obstacles. Our future work includes plans t

fuse Ladar images with video images to generate windows of interest in the video image

Classification of obstacles will be based on the information in the video windows.

6. Obstacle Map and List Module

We have adapted Kelly’s [10] grid obstacle map for representing obstacles for pat

planning and vehicle control. The map representation contains terrain surface slope

uncertainty measures, average depth information, and a list of obstacles for every grid i

the map. The path planning algorithm currently implemented in the 4D-RCS system [1

uses only a subset of the information contained in the map. It generates traversabilit

paths from the map by using a binary flag indicating whether or not the grid is an obstacl

a timestamp indicating when an obstacle was last updated, and an obstacle confidenc

value.

7. Conclusion

We have described an algorithm designed to detect obstacles in Ladar imagery. Th

Table 3 Detection of Positive Obstacles

Rock1 Rock2 Rock3 Rock4 Rock5 Rock

Clump

5 mph 13.9 m 20.2 m 18.6 m 25.0 m 12.8 m 31.8 m

10 mph 13.2 m 20.5 m 22.3 m 24.1 m 17.0 m 26.0 m

15 mph 15.0 m 23.2 m 22.2 m 26.6 m 13.7 m 30.6 m
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algorithm is one of a suite of algorithms being tested in order to evaluate different senso

that might be used for obstacle detection in the UGV program. We have characterized th

Ladar sensor being used, and described its performance on different types of surfaces.

In order to evaluate the obstacle detection algorithm, we collected extensive sets of Lad

data. These data were used as input for the obstacle detection algorithm. The algorithm

was able to detect both positive (rocks) and negative (ditches) obstacles. Detectability wa

determined to be a function of both the range to the obstacle and the size of the obstacl

The results of the evaluation tests are reported.

The obstacle detection algorithm was also tested in a real-time demonstration as part o

the 4D-RCS control system. In conjunction with the planner module and behavio

generation modules [1], we were able to detect obstacles on both the NIST grounds an

the NIKE test site while driving.

Acknowledgments

This work was sponsored by the Joint Robotics Program of the Under Secretary o

Defense for Acquisition and Technology (JRP OUSD(A&T)) and monitored by the U. S

Army Research Laboratory. We thank the members of the Intelligent Autonomou

Vehicles Group at NIST, ARL, the Toole Army Depot, the Aberdeen Test Center, and JP

for their efforts in the Perryman data collection. In particular, we thank Maris Jubert

Martin Herman, David Coombs, Billibon Yoshimi, Karl Murphy, Jim Gilsinn, Larr

Matthies, Todd Litman, Ken Owens, Tim Vong, Chuck Shoemaker, Jon Borenstein, Gar

Haas, Fred Eldridge, Ish Sherali, and Bob Brooks. We apologize to anyone inadvertentl

left off this list.
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