Autonomous driving paper index
Obstacle Avoidance using Monocular Depth Estimation for Small Drone Tello
One-line summary
In this study, we propose an obstacle avoidance program for the small drone Tello using depth estimation from a monocular camera image without using a depth sensor.
Engineering notes
Key topics: autonomous driving, depth estimation, monocular depth, monocular camera. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
In recent years, drones have become increasingly popular, and the market size has been expanding every year. Promising applications of drones include logistics, security, aerial photography, surveying, inspection of equipment, agriculture, and disaster relief And small drones are highly maneuverable due to their compact size, making them suitable for flying in narrow environments such as indoors. However, small drones have significant limitations on the types and performance of sensors they can carry due to their weight and price constraints. For example, to achieve autonomous flight of a drone, a depth sensor is necessary to measure the distance between the drone and the obstacle/target in front of it. However, many small drones do not have a depth sensor, or the measurable distance is very short. In this study, we propose an obstacle avoidance program for the small drone Tello using depth estimation from a monocular camera image without using a depth sensor. The feasibility of the proposed algorithm was evaluated in both simulation and real-world environments.
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