Machine Learning for Visual Navigation of Unmanned Ground Vehicles
This is an incremental review and comparison study for improving texture-based navigation in unmanned ground vehicles.
The paper tackles the problem of visual navigation for unmanned ground vehicles in cross-country environments by comparing three machine learning algorithms for classifying high-dimensional texture features, but no concrete results or numbers are provided.
The use of visual information for the navigation of unmanned ground vehicles in a cross-country environment recently received great attention. However, until now, the use of textural information has been somewhat less effective than color or laser range information. This manuscript reviews the recent achievements in cross-country scene segmentation and addresses their shortcomings. It then describes a problem related to classification of high dimensional texture features. Finally, it compares three machine learning algorithms aimed at resolving this problem. The experimental results for each machine learning algorithm with the discussion of comparisons are given at the end of the manuscript.