Drone swarms are transforming applications such as search and rescue, environmental monitoring, smart agriculture, and defense operations. A key challenge in swarm intelligence is accurate relative positioning among drones without relying heavily on GPS. To address this, researchers are increasingly turning to binocular stereo vision-based relative positioning algorithms.
Binocular stereo vision mimics human depth perception by using two synchronized cameras mounted on a drone. By capturing left and right images of neighboring drones, the system calculates depth through stereo matching and disparity estimation. This allows each drone to estimate the relative distance, orientation, and motion of others in real time.
The proposed relative positioning algorithm typically integrates feature extraction, epipolar geometry, triangulation, and pose estimation. Advanced methods combine stereo vision with Kalman filtering or visual–inertial fusion to improve robustness under dynamic flight conditions. Unlike GPS-based solutions, stereo vision works reliably in indoor, urban canyon, or GPS-denied environments.
One major advantage of this approach is its low communication overhead. Each drone performs onboard visual processing, enabling decentralized coordination and scalable swarm deployment. The algorithm also supports collision avoidance, formation control, and cooperative navigation, which are essential for autonomous swarm behavior.
Despite challenges such as lighting variations, motion blur, and computational cost, recent advances in deep learning-based stereo matching and lightweight vision processors are making binocular stereo vision a practical solution for real-world drone swarms.
Overall, binocular stereo vision-based relative positioning represents a promising step toward fully autonomous, GPS-independent drone swarms, opening new possibilities for intelligent multi-robot systems.
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