Research Article
Research and Application of Visual SLAM Based on Embedded GPU
@INPROCEEDINGS{10.1007/978-3-030-77569-8_1, author={Tianji Ma and Nanyang Bai and Wentao Shi and Lutao Wang and Tao Wu}, title={Research and Application of Visual SLAM Based on Embedded GPU}, proceedings={Quality, Reliability, Security and Robustness in Heterogeneous Systems. 16th EAI International Conference, QShine 2020, Virtual Event, November 29--30, 2020, Proceedings}, proceedings_a={QSHINE}, year={2021}, month={6}, keywords={Visual-SLAM Embedded Parallel computing CUDA GPU}, doi={10.1007/978-3-030-77569-8_1} }- Tianji Ma
Nanyang Bai
Wentao Shi
Lutao Wang
Tao Wu
Year: 2021
Research and Application of Visual SLAM Based on Embedded GPU
QSHINE
Springer
DOI: 10.1007/978-3-030-77569-8_1
Abstract
In automatic navigation robots, 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 color, 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. In this regard, this paper uses embedded hardware equipped with embedded GPU, combines CUDA-based GPU parallel computing and visual SLAM algorithm, finally, designs a parallelization scheme based on embedded GPU.
