Feature Transform Technique for Combining Landmark Detection and Tracking of Visual Information of Large Rain Forest Areas

Researchers have been spending a lot of effort in increasing the level of autonomy of Unmanned Aerial Systems (UASs). There is a sort of important scenarios where an autonomous drone would be very effective. One of these scenarios of applications is the long term monitoring of the Amazon rain forest. The uniform pattern of the canopy defines a mission difficult to be performed by a human operator. Imagine someone in front of a monitor seeing for hours long the very same thing: treetops. In such situation, an embedded vision system capable to drive the vehicle while taking decision of what is not fitting to a standard canopy pattern plays a critical role on both remotely operated and autonomous navigation modes. The goal of this work is to present a scheme based on image processing able to extract natural landmarks in forest areas, and to track them during posterior missions over the same area, as reference for the onboard navigation system. The scheme is composed of two main steps: 1) Nonrelevant features suppression based on wavelet, to eliminate the canopy uniform pattern, and 2) Key points extraction by SIFT algorithm, to extract new landmarks or to track existing ones. Preliminary results demonstrated that this system can increase the robustness of mission execution in scenarios where usually only GPS references are available.