1.合肥工业大学 计算机与信息学院 合肥 230601 2.合肥工业大学 大数据知识工程教育部重点实验室 合肥 230009 3.Department of Computer Science and Engineering, University of North Texas, Denton, Texas 76201, United States
LKDD-Net: Lightweight Keypoint and Deformable Descriptor Extraction Network
FANG Baofu1,2, ZHANG Keao1,2, WANG Hao1,2, YUAN Xiaohui3
1. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601 2. Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education of China, Hefei University of Technology, Hefei 230009 3. Department of Computer Science and Engineering, University of North Texas, Denton, Texas 76201, United States
Abstract：Keypoint extraction is a crucial step in visual simultaneous localization and mapping(VSLAM). Existing deep learning based keypoint extraction methods suffer from low efficiency and fail to meet real-time requirements. Furthermore, they do not provide the geometric invariance required by descriptors. To address this issue, a lightweight keypoint and deformable descriptor extraction network(LKDD-Net) is proposed. A lightweight network module is introduced in the backbone network to improve the efficiency of feature extraction, and then the deformable convolution module is applied to the descriptor decoder to extract deformable descriptors. LKDD-Net is capable of simultaneously obtaining both keypoint locations and deformable descriptors. To study the effectiveness of LKDD-Net, a visual odometry system based on LKDD-Net is designed. Experiments on HPatches public dataset and TUM public dataset show that LKDD-Net can run in real-time on GPUs with keypoint extraction time being as low as 8.3 ms, while maintaining high accuracy in various scenarios. The performance of the visual odometry system composed of LKDD-Net is superior to traditional vision and VSLAM systems based on deep learning keypoint extraction. The proposed method successfully tracks all six sequences in TUM public dataset, demonstrating stronger robustness.
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2024, Vol. 37 
