CNN-based Monocular Decentralized SLAM on embedded FPGA
2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)(2020)
摘要
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.
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关键词
CNN-based real-time DSLAM system,embedded FPGA,fixed-point fine-tune method,CNN-based monocular VO,fixed-point FPGA accelerator,environmental information,multirobot applications,visual odometry,CNN-based monocular decentralized SLAM,cross-component pipeline scheduling,DPR frequency,decentralized place recognition,decentralized visual simultaneous localization and mapping,6-DoF absolute pose
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