Abstract: With the development of artificial intelligence, precision machinery and computing technology, micro-unmanned system will play an important role in the future battlefield. To solve the lack of monocular visual odometry scale, micro robot power consumption and load limits, the monocular depth estimation technology is introduced and a low view dataset is collected. A convolutional neural network to predict depth information from a single image is built, and the structure of neural network model is optimized. The depth estimation with monocular visual odometry are combined and deployed on JetsonNano. Experiments show that the combined monocular visual odometry can recover scale information in a specific environment, and the power consumption on Jetson Nano can be kept a low level, which can provide some research basis for the concealable and lightweight deployment of micro-unmanned system in the future battlefield.

Key words: monocular visual odometry, monocular depth estimation, convolution neural network, structure optimization