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.
in autonomous vehicle is the situational understanding or terrain understanding and the complexity is increasing when the terrain is more unstructured.
Figure 2.1: Notional Approach to Top Level Systematics of Ground Mobile Robot Systems (Eicker, 2001).
As mentioned above, the emphasis on the terrain type that can be negotiated by an autonomous vehicle is moving toward highly unstructured terrain. The shift of the focus is mainly caused by the type of the mission that needed to be completed. Moreover, recent advanced technologies promote improvement of the hardware devices and these allow faster processing which cannot be achieved previously (Broggi, Bertozzi, Fascioli, & Conte, 1999).
One of the key tasks in rainforest navigation is terrain classification where the
Iraqi Desert
NM High
Desert Farmland
NM-like Mountains
European Forests
SE Asia Jungle
Year of High Likelihood of Robotic Mission Capability
Combat Service Support
Mission X
Situational Understanding
Terrain Complexity
2001
2005
2010
2015
type of terrain need to be identity before decision of which path to be taken is decided. Moreover, other key issues, such as system robustness and uncertainties present in rainforest terrain must be handled well. The following subsections will discuss on the state-of-the-art that is very much related to rainforest terrain of interest in this thesis.
2.3.1 Methods by DEMO III
One of the prominent projects is DEMO III by Jet Propulsion Laboratory (JPL) reported by Shoemaker et al (1998) and Bellutta et al (2000).
The project focus was on autonomous navigation in cross-country using vision- based perception. Although the terrain type was different from the rainforest terrain, many of the challenges presented were very much relevant and the proposed approach could be used in this thesis.
DEMO III attempted to use real-time stereo systems for geometric representation and obstacle detection. While the stereo systems were able to detect positive and negative obstacles, it is not able to characterize the terrain in term of its traversability. The main contribution of DEMO III project is that it had successfully introduces effective scene description based on geometric approach. The general problems in unstructured terrain navigation are evident as the flat-world assumption cannot be applied in this situation. In DEMO III, the ground in the terrain in consideration is relatively flat where the vehicle and sensors posed would not be affected dramatically. However, in rainforest
terrain, very bumpy road is expected and the vehicle guidance system must be able to compensate for the change of the vehicle pose from time to time.
While geometric presentation may be good to describe the terrain, the presence of compressible vegetation where the vehicle can run-over posed another challenging situation in unstructured terrain navigation. Some of the vegetation such as grass will be considered as obstacles in geometric point of view. To overcome the problems, DEMO III program performed the terrain classification based on color. The presence of compressible obstacles in DEMO III work was very much similar to the situation rainforest navigation and can be modified to be applied in rainforest navigation. However, the limitation of DEMO III is that relatively open ground is expected where most of the ground is present. In rainforest terrain, the ground terrain may be minimal and surrounded by compressible vegetation, thus in order for a successful navigation, these problems need to be solved.
Many similar projects were conducted on similar terrain as in DEMO III program, the difference of their approaches is the specific task processing for terrain classification and obstacle detection. The approaches were mainly to overcome common problems associated with color feature which are the chromatic problem and uneven illumination.
2.3.2 Methods by Darpa PerceptOR
Most of the research for autonomous vehicle is for military application and it is demonstrated by the prominent project and activities are mainly funded military agencies. The United States Defense Advanced Research Project Agency (DARPA) has been actively developing the reconnaissance capabilities of autonomous robot. For the past decade, DARPA (Krotkov, Fish, Jackel, McBride, Perschbacher, & Pippine, 2006) has engaged in Tactical Mobile Robotics program, DARPA PerceptOR and Darpa Grand Challenge program. While Tactical Mobile Robotics program focused more on urban environment, the following DARPA PerceptOR and Grand Challenge focus were off-road terrain. Darpa PerceptOr tried to sense the environment by creating 3-D model of the scene using stereo and ladar data. The idea was to create a 2-D map with obstacle identified in the map and to move towards the target area based on the map created.
The guidance system of PerceptOR was based on multiple sensors with differing field of view to detect different geometric aspects of obstacles and terrain. In addition to the sensors attached to the ground vehicle, PerceptOR was equipped with flying agent for early obstacles detection, mainly the steep negative obstacles or holes. Two SICK single axis ladars were placed in the forward direction of the vehicle with one scanning horizontally and another one scanning vertically. Two additional SICK ladar were also used for special purposes with one was mechanically rotated continuously to provide 360- degree view around the vehicle while another ladar looks rearward to provide
backup maneuvers. Digital video camera and stereo camera were used to provide colour information and alternative range data to the ladar.
In this work, the visual guidance system was divided into two sensing modes which were appearance sensing and geometric sensing. The geometric sensing module processes the range data to detect geometric hazard (i.e.
extreme slope, tree, etc.) while the appearance processes passive features to classify the terrain type. Both modules were fused to calculate the traversability cost and subsequently determining non-rigidity of the terrain. This work highlighted the importance of certain features to determine the compressibility and penetrability of sensed terrain. Compressibility is defined to be the object rigidity which is based on its material composition and penetrability is defined to be the object density based on the solidity and porosity of the object. This work also identified the color to be a powerful cue to determine the compressibility and the authors used neural network to perform the classification based on colors. The penetrability of an object was determined using the density points from the scanning ladar. In addition, the authors used stereo vision with traditional vertical horopter instead of group plane horopter.
It was highlighted that ground plane is not prominent in many complex scenes and difficult to isolate.
2.3.3 Methods by LAGR
There are many projects which are based on Learning Applied to Ground Robotics (LAGR) system developed at Carnegie-Mellon. It is a
platform for research which is equipped with two pairs of Bumblebee cameras and on-board GPS antenna for global localization (Happold & Ollis, 2006).
Thus, projects that use this platform only deal with higher level vision processing and planning algorithm. Alberts et al (2008) and Happold et al (2006) attempted to use supervised learning in LAGR platform for unstructured outdoor terrain. It should be highlighted that both authors were using different algorithms for their LAGR systems. Alberts et al (2008) managed to introduce fully autonomous and online learning to distinguish between ground plane and other objects while Happold et el (2006) claimed that their system will be able to perform safely on forested course based on their test. While LAGR system has proven to be successful in semi-structured environment, there is very little deployment of LAGR in unstructured terrain.
2.3.4 Methods by Manduchi et al (2005)
This work (Manduchi, Castano, Talukder, & Matthies, 2005) presented sensor algorithm that are suitable for cross country algorithm. The approach used a stereo camera and single-axis ladar for obstacles detection and terrain classification. The proposed methods were based on stereo range measurement, color-based classification and ladar data to label the detected obstacles according to a set of terrain classes.
The author attempts to detect obstacles using the 3-dimensional point cloud directly. Instead of extracting the reference ground plane, the method
above slope and height threshold will be considered as obstacles, otherwise traversable region. The impetus of this method was to minimize the effect the uneven ground that may affect the cameras tilt and roll. The color-based classification for the terrain typing concentrated mainly on the main obstacles in the terrain which are soil or rock, green (photosynthetic) vegetation and dry (non-photosynthetic) vegetation (which includes tree bark). Any terrain which was not included will be placed into a general group. Any classes with color ambiguities will be differentiated with the aid of the range data from radar.
This research is one of the pioneers of visual guidance for autonomous vehicle in complicated terrain and is very similar to the rainforest terrain of interest. The work did not attempt to solve the all navigation problems at once but to arbitrary solve important challenges one at a time. This approach worked very well as different sensors have their advantages in detecting certain obstacles over other obstacles. There are a few challenges that were highlighted by this work that require further investigation which are the varying illumination condition and color shift in atmospheric condition. There is a need for efficient and robust illumination compensation method for outdoor terrain as the illumination across the terrain is subject to shadow effect and irregular illumination. Consequently, it will affect the objects color across the scene.