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CNN-based Monocular Decentralized SLAM on embedded FPGA
| Content Provider | Semantic Scholar |
|---|---|
| Author | Yu, Jincheng Gao, Feng Cao, Jianfei Yu, Chao Zhang, Zhaoliang Huang, Zhengfeng Wang, Yu Yang, Huazhong |
| Copyright Year | 2020 |
| Abstract | Decentralized visual simultaneous localization and mapping (DSLAM) can share locations and environmental information between robots, which is an essential task for many multi-robot applications. The visual odometry (VO) is a basic component to estimate the 6-DoF absolute pose for robot applications. Decentralized place recognition (DPR) is a fundamental element to produce candidate place matches for sharing information among different robots. The goal of this paper is to build a CNN-based real-time DSLAM system on embedded FPGA platforms. Because of the high precision requirement of VO, the existing quantization methods can not be directly applied. We improve the fixed-point fine-tune method for the CNN-based monocular VO, which enables VO can be deployed on the fixed-point FPGA accelerator. We also explore the influence of the DPR frequency on the DSLAM results, and find out a proper DPR frequency to balance the accuracy and speed. A cross-component pipeline scheduling method is proposed to improve DPR frequency and further improve the final accuracy of DSLAM under the same hardware resource constraints. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://nicsefc.ee.tsinghua.edu.cn/media/publications/2020/RAW20_308.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
