Zhang, J.; Xiong, S.; Liu, C.; Geng, Y.; Xiong, W.; Cheng, S.; Hu, F. FPGA-Based Feature Extraction and Tracking Accelerator for Real-Time Visual SLAM. Sensors2023, 23, 8035.
Zhang, J.; Xiong, S.; Liu, C.; Geng, Y.; Xiong, W.; Cheng, S.; Hu, F. FPGA-Based Feature Extraction and Tracking Accelerator for Real-Time Visual SLAM. Sensors 2023, 23, 8035.
Zhang, J.; Xiong, S.; Liu, C.; Geng, Y.; Xiong, W.; Cheng, S.; Hu, F. FPGA-Based Feature Extraction and Tracking Accelerator for Real-Time Visual SLAM. Sensors2023, 23, 8035.
Zhang, J.; Xiong, S.; Liu, C.; Geng, Y.; Xiong, W.; Cheng, S.; Hu, F. FPGA-Based Feature Extraction and Tracking Accelerator for Real-Time Visual SLAM. Sensors 2023, 23, 8035.
Abstract
Due to the advantages of low latency, low power consumption and high flexibility of FPGA-based acceleration technology, it has been more and more widely studied and applied in the field of computer vision in recent years. An FPGA-based feature extraction and tracking accelerator for real-time visual odometry (VO) and visual simultaneous localization and mapping (V-SLAM) is proposed, which can realize the complete acceleration processing capability of the image front-end and directly output the feature point ID and coordinates to the backend. The accelerator consists of image preprocessing, pyramid processing, optical flow processing, and feature extraction and tracking modules. For the first time, it implements a hardware solution that combines features from accelerated segment test (FAST) corners with Gunnar Farneback (GF) dense optical flow, to achieve better feature tracking performance and provide more flexible technical route selection. In order to solve the scale invariance and rotation invariance lacking problem of FAST features, an efficient pyramid module with a five-layer thumbnail structure is designed and implemented. The accelerator is implemented on a modern Xilinx Zynq FPGA. The evaluation result shows that the accelerator can achieve stable tracking of features of violently shaking images, and is consistent with the results of MATLAB code running on PC. When operating at 100MHz, the accelerator can process 108 frames per second for 720P images and 48 frames per second for 1080P images. Compared to PC CPUs that consume seconds of time, the processing latency is greatly reduced to the order of milliseconds, making GF dense optical flow an efficient and practical technical solution on the edge side.
Engineering, Electrical and Electronic Engineering
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