Research on Deep Learning Navigation Algorithms for Helicopter Airborne Navigation

It is very hard to position helicopters in complex environments, and this severely limits their ability to navigate on their own. This paper proposes a navigation algorithm that uses a combination of different sensors and deep learning. It uses a special type of deep learning called ResNet50 and a special type of machine learning called LSTM. This algorithm extracts features of the environment and uses a Kalman filter to estimate the state of the system. The system is made more robust by merging information from multiple levels. The algorithm’s ability to maintain stable navigation in the face of faulty sensors is noteworthy, as is its use of an adaptive inference strategy that dynamically adjusts computational load. This strategy strikes a balance between performance and resource consumption. Experiments show that the plan works well in places where GPS is not available. This makes it much better for the helicopter to fly by itself, and it can be used in places like the army, for looking at places from the sky, and for helping people in danger.