Embedded Vision for Self-Driving on Forest Roads

by   Sorin Grigorescu, et al.

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.


page 1

page 2

page 3


Human perception in computer vision

Computer vision has made remarkable progress in recent years. Deep neura...

ClusterNet: Instance Segmentation in RGB-D Images

We propose a method for instance-level segmentation that uses RGB-D data...

Fruit Detection, Segmentation and 3D Visualisation of Environments in Apple Orchards

Robotic harvesting of fruits in orchards is a challenging task, since hi...

Deep Neural Network-based Cooperative Visual Tracking through Multiple Micro Aerial Vehicles

Multi-camera full-body pose capture of humans and animals in outdoor env...

Please sign up or login with your details

Forgot password? Click here to reset