CARG AI4UAV: Obstacle detection and avoidance

CARG AI4UAV: Obstacle detection and avoidance

CARG AI4UAV: Obstacle detection and avoidance

The research and technical developments in this project will enable UAS platforms to perform inspection, monitoring, and modeling the state of critical infra and superstructure in the BVLOS (Beyond Visual Line of Sight) mode, safely from a remote location. The threat detection and collision avoidance methods will provide the critical capability for BVLOS navigation of multiple drones to and around the infrastructure.

Publications

2024-06-04

Conferences

DroneRanger: Vision-Driven Deep Learning for Drone Distance Estimation

Published in 2024 International Conference on Unmanned Aircraft Systems (ICUAS)

by Azad, H, Mehta, V, Mantegh, I, Bolic, M

This paper introduces a novel approach to vision-based localization of drones using deep learning techniques.

2023-10-01

Conferences

Object Semantics Give Us the Depth We Need: Multi-task Approach to Aerial Depth Completion

Published in 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

by Gazani, SH, Dadboud, F, Bolic, M, Mantegh, I, Najjaran, H

The paper combines aerial depth completion and object detection tasks in a multi-task network.

Researchers

Principal Investigator

Miodrag Bolic
Principal Investigator

Post Doctoral

Graduate Student

Research Staff

Project Student

Nathan Bowness (Alumni)
Project Student

Collaborators

Varun_mehta
Collaborators

Academic Collaborators

Researcher Name	University/Institute	Position
I. Mantegh	NRC, Canada	Technology and Team Lead- AI Aerial Robotics & Counter UAS
H. Najjaran	University of Victoria	Professor of Mechanical Engineering

Industrial Partners

Company Name	Location
National Research Council Canada (NRC)	Ottawa, Canada