ISPRS Commission II, Vol.34, Part 3A „Photogrammetric Computer Vision“, Graz, 2002 
  
  
NEAR REAL-TIME ROAD CENTERLINE EXTRACTION 
C. K. Toth! and D. A. Grejner-Brzezinska? 
Center for Mapping, The Ohio State University, 1216 Kinnear Road, Columbus, OH 43212-1154, USA 
Department of Civil and Environmental Engineering and Geodetic Science’, The Ohio State University 
toth@cfm.ohio-state.edu 
Commission III 
KEY WORDS: Mobile Mapping, Image Sequence Processing, Direct Sensor Orientation, Real-Time Systems 
ABSTRACT: 
In this paper a new GPS/INS/CCD integrated system for precise monitoring of highway center and edge lines is presented. The 
system has been developed at The Ohio State University (OSU) for the Ohio Department of Transportation (ODOT). The positioning 
component of the system is based on tightly integrated GPS/INS (dual frequency GPS receiver and a high-accuracy strapdown INS), 
and the imaging component comprises a fast, color digital camera from Pulnix (TMC-6700, based on 644 by 482 CCD, with the 
acquisition rate up to 30 Hz), installed in a down-looking position. The high image rate provides sufficient overlap of the subsequent 
images at reduced highway speed. The stereo image data processing is supported in near real-time by on-the-fly navigation solution. 
In this paper, we discuss the design, algorithmic solution and operational aspects, as well as the calibration and performance analysis 
of the developed system. Feasibility of the application of real-time navigation data to on-the-fly image processing is also presented. 
In particular, a performance analysis of the integrated system, based on reference ground control, is discussed. 
1. INTRODUCTION 
Mobile Mapping Systems (MMS) have been developed since 
the early 1990s with a primary focus on the acquisition of the 
street environment data, i.e., man-made features and their 
attributes along the road corridor, as well as the topography. 
Over the years, MMS has evolved from a rather simple, low 
to modest accuracy mapping system, to modern state-of-the- 
art multisensor systems, incorporating an increasing amount 
of real-time operations. Mobile computing and wireless 
communication are considered two of the strongest trends in 
the modern computer industry. The proliferation of mobile 
computer and wireless technology used in modern MMS, 
combined with multiple, high resolution digital imaging 
sensors, bring fundamental changes to the ways the 
geoinformation data are acquired and analyzed: the data can 
be analyzed on-the-fly, and transferred to the data centers, 
where they can be transformed to the intelligent 
georeferenced information, and subsequently distributed to 
the users. 
The MMS presented in this paper, although classified as real 
time, does not fully follow the paradigm of mobile computing 
outlined above. The data are not transferred to the data 
analysis center, but rather part of the data processing is 
performed during the data collection, in real-time, by the 
onboard computer. Since the system is designed for mapping 
of center and edge lines of the highways, the instantaneous 
data transfer is not crucial. The real-time image processing is 
designed to limit the amount of data stored for further 
processing. In particular, the linear features can be extracted 
and tracked from the imagery on-the-fly, using real-time 
navigation information, which can effectively support the 
formation of stereo-pairs. Therefore, the real-time part of the 
image processing is only concerned with the relative 
orientation (RO). Tthe final processing can be done in post- 
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mission mode when more precise navigation data become 
available. In this paper, a discussion related to the recently 
performed tests demonstrating the achievable accuracy in real 
time is included, while more details on the system design and 
the concept of real-time processing can be found in (Toth and 
Grejner-Brzezinska, 2001a and 2001b; Grejner-Brzezinska, 
Toth and Yi, 2001; Grejner-Brzezinska, Yi and Toth, 2001; 
Grejner-Brzezinska and Toth, 2002). 
2. SYSTEM DESIGN AND IMPLEMENTATION 
The positioning module of this system is based on a tight 
integration of dual frequency differential GPS phases and raw 
IMU data provided by a  medium-accuracy and 
high-reliability strapdown Litton LN-100 inertial navigation 
system. LN-100 is based on Zero-lock™ Laser Gyro (ZLG™) 
and A-4 accelerometer triad (0.8 nmi/h CEP, gyro bias — 
0.003°/h, accelerometer bias — 25ug). An optimal 21-state 
centralized Kalman filter estimates errors in position, velocity, 
and attitude, as well as the errors in the inertial sensors. In 
addition, the basic 21-state vector can be augmented by the 
additional states representing GPS differential ionospheric 
correction terms, which are estimated (per satellite pair, as 
double difference mode is used) when the base-rover 
separation exceeds 10 km distance. The primary filter design 
follows the concept of AIMS™ (Grejner-Brzezinska et al., 
1998; Toth and Grejner-Brzezinska, 1998), developed earlier, 
which has been modified and extended to accommodate 
needs of precision navigation in urban environments. These 
augmentations primarily include the implementation of the 
static INS calibration (ZUPT mode) and the extension of the 
measurement update module to include the pseudolite data 
(Grejner-Brzezinska et al, 2002), as well as further 
processing optimization. Under favorable GPS constellation 
(minimum of 5-6 satellites), the estimated standard deviations