A Survey of Computer Vision Detection, Visual SLAM Algorithms, and Their Applications in Energy-Efficient Autonomous Systems

Lu Chen, Gun Li (), Weisi Xie, Jie Tan, Yang Li, Junfeng Pu, Lizhu Chen, Decheng Gan and Weimin Shi ()
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Lu Chen: School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China
Gun Li: School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China
Weisi Xie: School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China
Jie Tan: School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China
Yang Li: School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China
Junfeng Pu: School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China
Lizhu Chen: School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China
Decheng Gan: School of Electronic Information Engineering, Yangtze Normal University, Chongqing 408100, China
Weimin Shi: School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China

Energies, 2024, vol. 17, issue 20, 1-38

Abstract: Within the area of environmental perception, automatic navigation, object detection, and computer vision are crucial and demanding fields with many applications in modern industries, such as multi-target long-term visual tracking in automated production, defect detection, and driverless robotic vehicles. The performance of computer vision has greatly improved recently thanks to developments in deep learning algorithms and hardware computing capabilities, which have spawned the creation of a large number of related applications. At the same time, with the rapid increase in autonomous systems in the market, energy consumption has become an increasingly critical issue in computer vision and SLAM (Simultaneous Localization and Mapping) algorithms. This paper presents the results of a detailed review of over 100 papers published over the course of two decades (1999–2024), with a primary focus on the technical advancement in computer vision. To elucidate the foundational principles, an examination of typical visual algorithms based on traditional correlation filtering was initially conducted. Subsequently, a comprehensive overview of the state-of-the-art advancements in deep learning-based computer vision techniques was compiled. Furthermore, a comparative analysis of conventional and novel algorithms was undertaken to discuss the future trends and directions of computer vision. Lastly, the feasibility of employing visual SLAM algorithms in the context of autonomous vehicles was explored. Additionally, in the context of intelligent robots for low-carbon, unmanned factories, we discussed model optimization techniques such as pruning and quantization, highlighting their importance in enhancing energy efficiency. We conducted a comprehensive comparison of the performance and energy consumption of various computer vision algorithms, with a detailed exploration of how to balance these factors and a discussion of potential future development trends.

Keywords: computer vision; object detection; visual tracking; energy efficiency; visual SLAM; deep learning; driverless robotic vehicles (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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