Research and Application of Visual SLAM Based on Embedded GPU

2021 ◽  
pp. 3-21
Author(s):  
Tianji Ma ◽  
Nanyang Bai ◽  
Wentao Shi ◽  
Lutao Wang ◽  
Tao Wu
Keyword(s):  
Visual Slam ◽  
Embedded Gpu

Evaluating the Power Efficiency of Visual SLAM on Embedded GPU Systems

2019 ◽  
Author(s):  
Tao Peng ◽  
Dingnan Zhang ◽  
Ruixu Liu ◽  
Vijayan K. Asari ◽  
John S. Loomis
Keyword(s):  
Power Efficiency ◽  
Visual Slam ◽  
Embedded Gpu

scholarly journals Research on the Application of Visual SLAM in Embedded GPU

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Tianji Ma ◽  
Nanyang Bai ◽  
Wentao Shi ◽  
Xi Wu ◽  
Lutao Wang ◽  
...  
Keyword(s):  
High Performance ◽  
Three Dimensional ◽  
Image Data ◽  
Operating Efficiency ◽  
Visual Slam ◽  
Mobile Platforms ◽  
Unknown Environment ◽  
Localization And Mapping ◽  
Slam Algorithm ◽  
Embedded Gpu

In the automatic navigation robot field, robotic autonomous positioning is one of the most difficult challenges. Simultaneous localization and mapping (SLAM) technology can incrementally construct a map of the robot’s moving path in an unknown environment while estimating the position of the robot in the map, providing an effective solution for robots to fully navigate autonomously. The camera can obtain corresponding two-dimensional digital images from the real three-dimensional world. These images contain very rich colour, texture information, and highly recognizable features, which provide indispensable information for robots to understand and recognize the environment based on the ability to autonomously explore the unknown environment. Therefore, more and more researchers use cameras to solve SLAM problems, also known as visual SLAM. Visual SLAM needs to process a large number of image data collected by the camera, which has high performance requirements for computing hardware, and thus, its application on embedded mobile platforms is greatly limited. This paper presents a parallelization method based on embedded hardware equipped with embedded GPU. Use CUDA, a parallel computing platform, to accelerate the visual front-end processing of the visual SLAM algorithm. Extensive experiments are done to verify the effectiveness of the method. The results show that the presented method effectively improves the operating efficiency of the visual SLAM algorithm and ensures the original accuracy of the algorithm.

scholarly journals Unsupervised Learning-Based Depth Estimation-Aided Visual SLAM Approach

2019 ◽  
Vol 39 (2) ◽  
pp. 543-570 ◽  
Author(s):  
Mingyang Geng ◽  
Suning Shang ◽  
Bo Ding ◽  
Huaimin Wang ◽  
Pengfei Zhang
Keyword(s):  
Unsupervised Learning ◽  
Depth Estimation ◽  
Visual Slam

Real-time CAD-level surface of revolution reconstruction on visual slam

2020 ◽  
Author(s):  
Feng Li ◽  
Bin He ◽  
Gang Li ◽  
Ming Ma ◽  
Jian Li ◽  
...  
Keyword(s):  
Real Time ◽  
Level Surface ◽  
Visual Slam ◽  
Surface Of Revolution

DP-SLAM: A Visual SLAM with Moving Probability Towards Dynamic Environments

2021 ◽  
Author(s):  
Ao Li ◽  
Jikai Wang ◽  
Meng Xu ◽  
Zonghai Chen
Keyword(s):  
Dynamic Environments ◽  
Visual Slam
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