Abstract
This paper describes an obstacle avoidance system for low-cost Unmanned Aerial Vehicles (UAVs) using vision as the principal source of information through the monocular onboard camera. For detecting obstacles, the proposed system compares the image obtained in real time from the UAV with a database of obstacles that must be avoided. In our proposal, we include the feature point detector Speeded Up Robust Features (SURF) for fast obstacle detection and a control law to avoid them. Furthermore, our research includes a path recovery algorithm. Our method is attractive for compact MAVs in which other sensors will not be implemented. The system was tested in real time on a Micro Aerial Vehicle (MAV), to detect and avoid obstacles in an unknown controlled environment; we compared our approach with related works.
Highlights
30 kg; Category IV: mini; they have a payload of 2 kg, are electrically operated, low cost, have easy maintenance and safe operation; and Category V: Micro Air Vehicles (MAVs) have a payload lower than 100 grams and are used in navigation and detection
One of the problems in the teleoperation of Unmanned Aerial Vehicles (UAVs) is the loss of pilot visibility and/or the signal of the Global Positioning System (GPS); the autonomous system is an alternative for solving this issue
Our work proposes a real-time obstacle detection algorithm based on feature points and an offline modeling of the MAV for designing a controller for fixed and mobile obstacle avoidance in an unknown controlled environment
Summary
Unmanned Aerial Vehicles (UAVs) are applied in several applications, like surveillance, mapping, journalism, transport, rescue, military applications and environments where a human cannot access, such as radioactive areas, toxic environments and handling of dangerous objects [1]. The autonomous systems include motion planning, path tracking, obstacle avoidance, target detection and other areas [3]. These systems require sensing, state estimation, perception and knowledge of the situation. The perception is used to detect and avoid obstacles in real. Our proposal works with a perception method based on feature points for obstacle detection and a proportional control to avoid them, using a monocular camera without depending on other sensors.
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