  Forest roads in Romania are unique natural wildlife sites used for recreation
by countless tourists. In order to protect and maintain these roads, we propose
RovisLab AMTU (Autonomous Mobile Test Unit), which is a robotic system designed
to autonomously navigate off-road terrain and inspect if any deforestation or
damage occurred along tracked route. AMTU's core component is its embedded
vision module, optimized for real-time environment perception. For achieving a
high computation speed, we use a learning system to train a multi-task Deep
Neural Network (DNN) for scene and instance segmentation of objects, while the
keypoints required for simultaneous localization and mapping are calculated
using a handcrafted FAST feature detector and the Lucas-Kanade tracking
algorithm. Both the DNN and the handcrafted backbone are run in parallel on the
GPU of an NVIDIA AGX Xavier board. We show experimental results on the test
track of our research facility.
