The works on visual guidance system for structured outdoor terrain usually involve vehicle maneuvering in urban road with specific task such as obstacles detection (usually cars and pedestrian) and avoidance, landmark detection (i.e. road sign and traffic light) and road following. Works by Dickmanns (2002) and DeSouza et al (2002) provide comprehensive review of machine for road vehicle. Although the terrain in consideration of this thesis is very much different from navigation in structured road, it is important to highlight the state-of-the-art in this area as most of the functional systems in unstructured terrain are derived from structured terrain.
One of the key tasks in structured environment is road detection. The vehicle will detect the road and perform road-following. The key feature is road markings on the road and the visual guidance system will detect the lines on the road that separate the lanes and maintain the vehicle position on the road. To date, road-following is a relatively mature technology with many successful implementations such as Navlab projects, ALVINN projects and ARGO projects.
2.2.1 Methods based on NAVLAB and ALVINN
One of the earliest autonomous outdoor vehicles reported is by the Carnegie-Mellon University named Carnegie-Mellon Navigation Lab (Navlab) (Thorpe, Hebert, Kanade, & Shafer, 1988). The vehicle was a testbed which
was used to test perception modules and navigation modules. It was an onboard platform on an actual vehicle for testing in real-world environments. Over the years, it had gone through various implementations with the later implementation was called Autonomous Land Vehicle in a Neural Network (ALVINN) (Pomerleau, 1989) where the focus was on the usage of neural network to increase the robustness in road detection.
Navlab was equipped with color vision for lane tracking and scanning laser range finder for obstacle detection and avoidance. The Navlab approaches include edge detection and image color classification. It attempted to extract the edge of the road with the assumption the road is well structured and to classify the road base on the color on road and non-road area. This Navlab also attempted to fuse the texture cues with color vision features to identity road edges. The texture feature was used to aid in the road detection with the assumption that the road region will appear smoother than the non-road region.
Commonly, earlier attempts tend to identify the structured features present in the road to determine the traversable region. The problem of changes in illumination was highlighted where the authors tried to solve it by pre- determining the cluster colors using separate Gaussian clusters.
The ALVINN project was a system equipped in NAVLAB as a neural- network-based navigation system (Pomerleau, 1989). There were several prominent works on ALVINN system to improve the performance of the system which can be found in Batavia et al (1996) and Jochem et al (1995).
Generally, the system approach was to use artificial intelligence in autonomous
vehicle to drive automatically. The project had successfully traversed on the highway with highway speed. It utilized the neural network to learn on-line by
“observing how human drive”. The digitized signal from camera is fed into neural network module and the output of the training is the steering direction which is fed to the car steering control.
Both the approaches based on the Navlab and ALVINN were only limited to structured road with many assumptions which are not applicable or suitable to rainforest terrain. The learning method used in ALVINN requires a lot of training scheme where certain scenarios can be expected and many assumptions need to be made. On the contrary to rainforest terrain, a structured road cannot be expected to be present and the assumption used on structured road cannot be applied. However, the approach had triggered the interest in using artificial intelligence in autonomous vehicle navigation.
2.2.2 Methods based on ARGO
The ARGO (Broggi, 1999) project is one of the most mature lane detection systems which aim for active safety system and automatic pilot for road vehicle. The system incorporated only passive sensors such as camera, proprioceptive sensor and commercial personal computer in its prototype. Two cameras were mounted at the top corners of the windscreen and a speedometer were used to detect the velocity of the vehicle. The images acquired from the stereo cameras and speedometer were fed to computing system located in vehicle boot. After computation and processing, the output was fed to the
actuator of the car. The computing system was able to compute lane geometry for lane following, detect common obstacle on road and detect lead vehicle.
Recently, much of this area of research is concentrated on more complex road condition which takes into the consideration the traffic signs, pedestrian and etc.
Similar to Navlab and ARGO project, the systems cannot be fully applied to rainforest terrain. However, it is important to highlight that all the projects discussed previously relied heavily on vision system to detect the road and obstacles. Evidently, it can be said that the vision system is a key-enabling technology to autonomous vehicle navigation and obstacle detection.