Low Power Visual Odometry Technology Based on Monocular Depth Estimation

Authors

Ma Rong, 1. Science and Technology on Special System Simulation Laboratory, Beijing 100854, China; ;
Qiurui Chen, 1. Science and Technology on Special System Simulation Laboratory, Beijing 100854, China; ;
Zhang Han, 1. Science and Technology on Special System Simulation Laboratory, Beijing 100854, China; ;
Mei Zheng, 1. Science and Technology on Special System Simulation Laboratory, Beijing 100854, China; ;
Wang Rui, 2. BeiHang University, Beijing 100191, China; ;
Wei Wei, 3. The Fourth Military Representative Office of the Air Force Armament Department in Beijing, Beijing 100041, China;

Abstract

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.

Recommended Citation

Rong, Ma; Chen, Qiurui; Han, Zhang; Zheng, Mei; Rui, Wang; and Wei, Wei (2022) "Low Power Visual Odometry Technology Based on Monocular Depth Estimation," Journal of System Simulation: Vol. 33: Iss. 12, Article 25.
DOI: 10.16182/j.issn1004731x.joss.21-FZ0863
Available at: https://dc-china-simulation.researchcommons.org/journal/vol33/iss12/25

First Page

3001

Revised Date

2021-08-25

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.21-FZ0863

Last Page

3011

CLC

TP391.9

Recommended Citation

Ma Rong, Chen Qiurui, Zhang Han, Mei Zheng, Wang Rui, Wei Wei. Low Power Visual Odometry Technology Based on Monocular Depth Estimation[J]. Journal of System Simulation, 2021, 33(12): 3001-3011.

DOI

10.16182/j.issn1004731x.joss.21-FZ0863