Well there are less than 9 weeks for the project's deadline, so there will be few more new objectives to add.
First i think i will add at lest 2 more distance-based segmentation algorithms:
- Segmentation algorithm used by Santos, et al., 2003, this algorithms is based on the approach used by Dietmayer (already used in my project) but with and additional parameter β. This parameter aims to reduce the dependence of the segmentation with respect to the distanc between the laser range finder and the objects. This is the threshold condition:
Where C0 and C1 are the same as in the Dietmayer approach.
- The other approach is used by Lee, et al. 2001, is a simple method of segmentation with the following threshold condition:
The objective of adding these 2 more algorithms is just to test as much segmentation algorithms as possible to enrich the project, as the the ros and the rviz platforms are built it does't add that much work to add more algorithms.
There might be a novelty in this project which is the Segmentation of Vegetation
In the last post, i referred this issue, as the laser range points in a vegetation area are kind of disperse random point cloud, the potential final goal is to preform the segmentation of these dispersed clusters.
I will try to implement the popular k-means algorithm using only the small objects (1 or 2 points) in the scan. If the points the clusters given by the k-means have few dispersion the we have a group(a brush or a tree).
- Santos, S., J.E. Faria, F. Soares, R. Araujo and U.Nunes (2003). Tracking of Multi-Obstacles with Laser Range Data for Autonomous Vehicles. In: Proc. 3rd National Festival of RoboticsScientific Meeting (ROBOTICA), pp. 59-65, Lisbon, Portugal.
- Lee, K.J. (2001). Reactive navigation for an outdoor autonomous vehicle. Master Thesis. University of Sydney, Department of Mechanical and Mechatronic Engineering.