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A Real-time System for Road Management 3. IMU/GPS INTEGRATION Although the GPS provides very accurate position information (and attitude - when three GPS antennas are used) it would not be safe to fully rely on it. In cities, forests and tunnels the GPS signal is not available; so, another positioning system is needed. An IMU consists of a triad of accelerometers and a triad of gyroscopes. When the outputs of these six sensors are integrated with respect to time, the displacement and attitude are determined; however, these sensors suffer from biases and scale factor errors that cause the solution to degrade fast in time. To tackle these problems, the GPS and the IMU are integrated in a Kalman Filter; since there are two GPS antennas, the external measurements of the Kalman Filter are GPS derived position and azimuth. This IMU/GPS integration, thus, provides a direct georeferencing for the camera (three position components and three attitude angles) in all circumstances. For details in the IMU/GPS integration, the reader can refer to Schwarz and Wei (2000), Titterton and Weston (1997). The development of real-time IMU/GPS Kalman Filter is not trivial. However, the GPS RTK procedure presented above facilitates one aspect of this integration. The other aspect is the real-time monitoring of reliability and integrity of the solution. This is provided by applying advanced statistical techniques and reliability testing. The accuracy of the IMU/GPS integration depends on two factors:
To test our system we conducted many surveys in Switzerland, where forests and tunnels are common. The GPS outrages in these environments lasted for less than a minute with a very few cases exceeded the one minute. Simulated GPS outages were carried out to study the error in the IMU position. Figure 6 shows the error in the position derived from the stand-alone IMU for one minute.  Figure 6. Error in IMU-derived position The true multi-base station processing has become a reality in many receivers. In this work, we highly consider the multi-base station processing because of the nature of our mission, where the photobus moves constantly and needs to get the DGPS correction from the nearest reference station. This is done by a training procedure and algorithmic additions that take into account all the reference stations in the vicinity. Before the survey, the reference stations in the vicinity are defined so that the algorithm can switch from one reference station to the other depending on the optimal distance that provides the best DGPS corrections. 4. ENHANCING THE IMAGING COMPONENT BY CMOS TECHNOLOGY 4.1 Introduction of CMOS technology Over the past five years, there has been a growing interest in CMOS image sensors. Such imagers can be made with standard silicon processes in high-volume foundries. Peripheral electronics, i.e. digital logic, clock drivers, or analog-to-digital converters, are readily integrated with the same fabrication process. To achieve these benefits, the CMOS sensor’s architecture is arranged more like a memory cell or flat-panel display (Figure 7). Each photosite contains a photodiode that converts light to electrons, a charge-to- voltage conversion section, a reset and select transistor and an amplifier section. Overlaying the entire sensor is a grid of metal interconnects to apply timing and readout signals, and an array of column output signal interconnects. The column lines connect to a set of decode and readout (multiplexing) electronics that are arranged by column outside of the pixel array (Mendis et al., 1994). This architecture allows the signals from the entire array, from subsections, or even from a single pixel to be read by a simple X-Y addressing technique.  Figure 7. CMOS structure 4.1 Power consumption Whereas CCD cameras require numerous chips for the sensor, drivers and signal conditioning, CMOS technology allows the manufacture of imaging devices that can be monolithically integrated as mentioned earlier. The reduced number of parts required has a positive impact on the power consumption while decreasing system size and complexity (Cho et al., 2001). 4.2 Noise and dark current Fixed Pattern Noise (FPN) and random temporal noise eventually limit the performance of image sensors. FPN is time-dependent and arises from component mismatch due to process variations. Calibration or appropriate electronics can cancel FPN as shown in Figures 8 and 9.  Figure 8. CMOS frame without FPN correction  Figure 9. CMOS frame with FPN correction The temporal noise includes:
4.4 Bandwidth and saturation CCDs rely on a process that can leak charge to adjacent pixels when the CCD registers overflows. Thus, bright light blooms cause unwanted streaks on the image. CMOS architecture is inherently less sensitive to this effect. Moreover, smear that is caused by charge transfer in the CCD under illumination is non-existent with CMOS. 4.5 Introduction of a CMOS camera to Photobus The Ethercam CMOS camera is a complete vision system that combines the functions of image acquisition and digital processing in a compact form (Figure 10). Interpreted results or raw images can be transmitted remotely to host computers through a 10-Mbit Ethernet connection. A serial RS232 interface and three optoisolated I/O lines allow synchronized image acquisition. Consequently, the connection to the computer hosting the mobile mapping software is simple and without a need a frame-grabber.  Figure 10. The Ethercam The Ethercam embeds a Linux operating system, which supports high level programming languages and thus a wide range of vision libraries. Keeping the computation tasks within the camera helps its host computer to better focus upon time-critical tasks such as the synchronisation of the GPS-RTK position data with the captured frames. The imaging sensor mounted on the Ethercam is a monochromatic matrix of VGA size, i.e. 640 ×480 pixels. It presents a dynamic range of six decades (120 dB) as a consequence of the logarithmic response of pixels to light intensity (Fossum, 1997). Such a response implies that relative variations of light intensity (.I/I) are perceived with constant sensitivity over the entire range. This property is particularly useful for the analysis of outdoor scenes where light intensity varies substantially from high sunny conditions (100 000 Lux) to dark shadows (10 Lux). The previous surveys of the road with the CCD sensors showed that most automatic algorithms of centreline detection are deceived by varying light conditions (Gilliéron et al., 2001). In fact, using fast low-level filtration techniques, such as binarization, reject under-exposed pixels of shadowed areas or promote over-saturated pixels under the direct sunlight. Due to its logarithmic response to illumination and unlike a CCD camera, the Ethercam CMOS sensor allows the reproduction of the outdoor scenes without any imperfections such as blooming, smearing, or time lag (Figure 11). Hence, CMOS reproduction of the reality simplifies the methodology for extracting the pixel coordinates of the road centreline.  Figure 11. Light and shadow on the same frame, as seen by CCD (left) and CMOS (right) cameras. 5. APPLICATIONS OF THE SYSTEM AND CONCLUSIONS Road management is an essential part of transportation engineering and it requires a fast and accurate road mapping. The system proposed here is an integrated system from which the user can study the road geometry and map the road features, which are considered as two critical characteristics for managing safety requirements and warning procedures (Gontran et al., 2005). We explored two promising solutions to map the road centreline in real-time: Internet-based RTK and a logarithmic CMOS camera. The presented enhancements due to the CMOS-based Ethercam relieved the workstation hosting the mobile mapping software from image grabbing and processing. Consequently, most of the computer time can be dedicated to more critical tasks and the processor can be given further help by a real-time operating system that allows a definition of a hierarchy of tasks. Data storage and visualisa-tion of the trajectory and the heading of Photobus are given a lesser priority than pixel georeferencing while the data synchronisation with GPS time is completed first (Figure 13). To implement our future real-time mobile mapping system, we chose the RTLinux kernel that is well-established in the academic community. This kernel accomplishes real-time performances by monitoring device drivers, the use of interrupt disabling and virtual memory operations that are sources of unpredictability. In fact, the RTLinux kernel lies between the standard Linux kernel and the hardware, whereas the standard Linux kernel sees the real-time layer as the actual hardware. Theoretically, the user can introduce and set priorities to every task. Consequently, we can achieve correct timing for the processes by deciding on the scheduling algo-rithms, priorities and frequency of execution (Yodaiken, 1999).  Figure 13. Real-time approach for Photobus Further development will involve a tighter integration of the sensors within the scheduled automatic algorithms of RTLinux. This should lead to the implementation of a quality check of mapped data directly in the field. REFERENCES
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