Scene perception and traversability analysis are real challenges for autonomous driving systems. In the context of off-road autonomy, there are additional challenges due to the unstructured environments and the existence of various vegetation types. It is necessary for the Autonomous Ground Vehicles (AGVs) to be able to identify obstacles and load-bearing surfaces in the terrain to ensure a safe navigation (McDaniel et al. 2012). The presence of vegetation in off-road autonomy applications presents unique challenges for scene understanding: 1) understory vegetation makes it difficult to detect obstacles or to identify load-bearing surfaces; and 2) trees are usually regarded as obstacles even though only trunks of the trees pose collision risk in navigation. The overarching goal of this dissertation was to study traversability analysis in unstructured forested terrains for off-road autonomy using LIDAR data. More specifically, to address the aforementioned challenges, this dissertation studied the impacts of the understory vegetation density on the solid obstacle detection performance of the off-road autonomous systems. By leveraging a physics-based autonomous driving simulator, a classification-based machine learning framework was proposed for obstacle detection based on point cloud data captured by LIDAR. Features were extracted based on a cumulative approach meaning that information related to each feature was updated at each timeframe when new data was collected by LIDAR. It was concluded that the increase in the density of understory vegetation adversely affected the classification performance in correctly detecting solid obstacles. Additionally, a regression-based framework was proposed for estimating the understory vegetation density for safe path planning purposes according to which the traversabilty risk level was regarded as a function of estimated density. Thus, the denser the predicted density of an area, the higher the risk of collision if the AGV traversed through that area. Finally, for the trees in the terrain, the dissertation investigated statistical features that can be used in machine learning algorithms to differentiate trees from solid obstacles in the context of forested off-road scenes. Using the proposed extracted features, the classification algorithm was able to generate high precision results for differentiating trees from solid obstacles. Such differentiation can result in more optimized path planning in off-road applications.

Natural forests are characterised by great vegetation diversity and create habitats for a major part of Earth’s terrestrial organisms. Plantation forests, which are mainly composed of a few genera of fast-growing trees, constitute an increasing fraction of global forests, but they only partly compensate for loss of area, habitat and ecological functions in natural forests. Plantation forests established near natural forests can be expected to serve as buffers, but they seem to be relatively poor in invertebrate species and it is not clear why. This bachelor’s degree project aimed at establishing the ecological and edaphic factors that correlate with soil invertebrate diversity in dry upland forests and surrounding plantation forests in eastern Africa. Some aspects of the above-ground vegetation heterogeneity were investigated since this was assumed to influence the heterogeneity of the soil environment, which is considered as critical for soil biodiversity. The obtained knowledge may be valuable in conservation activities in East African forests, which are threatened by destruction, fragmentation and exotic species. The study area was Karura Forest, a dry upland forest in Nairobi, Kenya. Three different sites were investigated; a natural forest site characterized by the indigenous tree species Brachylaena huillensis and Croton megalocarpus, and two different plantation forest sites, characterized by the exotic species Cupressus lusitanica and Eucalyptus paniculata, respectively. For each forest type, six plots were visited. Soil invertebrates were extracted from collected soil and litter samples by sieving and Berlese-Tullgren funnels. The invertebrates were identified, and the taxonomic diversity calculated at the order level. The ecological and edaphic factors, measured or calculated for each plot, were tree species diversity, ratio of exotic tree species, vertical structure of trees, vegetation cover, vegetation density, litter quality, soil pH, soil temperature and soil moisture. One-way ANOVA was used to compare soil invertebrate diversity and other variables between different forest types. Akaike’s Information Criterion and Multiple Linear Regression were used to establish linear models with variables that could explain measured variations of the diversity. There was some evidence for higher soil invertebrate diversity in natural forests than in surrounding plantation forests. The abundance of soil invertebrates was also clearly higher in natural forests, which indicates that natural forests are more important than plantation forests for conservation of soil invertebrate populations. Soil invertebrate diversity (in terms of number of orders present) was found to be influenced by forest type and litter quality. The diversity was higher at places with high amounts of coarse litter, which here is considered as more heterogenous than fine litter. The dependence on forest type was partly a consequence of differences in soil pH since Eucalyptus trees lower soil pH and thereby also soil biodiversity. No relation to heterogeneity of above-ground vegetation was found. For future conservation activities in Karura Forest Reserve it is recommended to continue removing exotic plant species and replanting indigenous trees, to prioritize the removal of Eucalyptus trees before Cypress trees, to only remove a few trees at a time and to establish ground vegetation when doing so.