Recently, there are many autonomous navigation applications done in outdoor environment. However, safe navigation is still a daunting challenge in terrain containing vegetation. Thus, a study on vegetation detection for outdoor automobile navigation is investigated in this work. At the early state of our research, we focused on the segmentation of LADAR data into two classes by using local three-dimensional point cloud statistics. The classes are: scatter to represent vegetation such as tall grasses, bushes and tree canopy, surface to capture solid objects like ground surface, rocks or tree trunks. However, the only use of 3D features would never result a real robust vegetation detection system because of lacking color information. We, hence, propose a 2D-3D combination approach which can utilize the complement of three-dimensional point distribution and color descriptor. Firstly, 3D point cloud is segmented into regions of homogeneous distance. The local point distribution is then analyzed for each region to extract scatter features. Secondly, a coarse 2D-3D calibration needs to be implemented in order to map the regions to the corresponding color image. Then, color descriptors are studied and applied to each region and considered as color features. Those all scatter and color features will be trained by Support Vector Machine to generate vegetation classifier. Finally, we will show the out-performance of this approach in comparison with more conventional approaches.

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Trees which are growing beneath high-voltage transmission lines have to be cut down if the distance to the conductor rope gets too close. It is very hard to find those if only single trees are affected. Chopping simply all trees under a transmission line, which was done in former times, is not a desirable solution for environmental and economic reasons. In this paper a solution is shown which uses an unmanned aerial vehicle and a laser scanner to generate a 3d point-cloud representing the trees and the conductor ropes. Using this point-cloud the decision for cutting down a specific tree can easily be made.

The paper introduces an active way to detect vegetation which is at front of the vehicle in order to give a better decision-making in navigation. Blowing devices are to be used for creating strong wind to effect vegetation. Motion compensation and motion detection techniques are applied to detect foreground objects which are presumably judged as vegetation. The approach enables a double-check process for vegetation detection which was done by a multi-spectral approach, but more emphasizing on the purpose of passable vegetation detection. In all real world experiments we carried out, our approach yields a detection accuracy of over 98%. We furthermore illustrate how the active way can improve the autonomous navigation capabilities of autonomous ground vehicles.

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