Abstract
The Simultaneous Localization and Mapping (SLAM) algorithm is a hotspot in robot application research with the ability to help mobile robots solve the most fundamental problems of “localization” and “mapping”. The visual semantic SLAM algorithm fused with semantic information enables robots to understand the surrounding environment better, thus dealing with complexity and variability of real application scenarios. DS-SLAM (Semantic SLAM towards Dynamic Environment), one of the representative works in visual semantic SLAM, enhances the robustness in the dynamic scene through semantic information. However, the introduction of deep learning increases the complexity of the system, which makes it a considerable challenge to achieve the real-time semantic SLAM system on the low-power embedded platform. In this paper, we realized the high energy-efficiency DS-SLAM algorithm on the Field Programmable Gate Array (FPGA) based heterogeneous platform through the optimization co-design of software and hardware with the help of OpenCL (Open Computing Language) development flow. Compared with Intel i7 CPU on the TUM dataset, our accelerator achieves up to 13× frame rate improvement, and up to 18× energy efficiency improvement, without significant loss in accuracy.
Highlights
Mobile robots are widely used in fields such as industry, agriculture, medical and services [1,2], but are well used in dangerous situations such as urban security [3], national defense [4] and space detection [5,6]
The proposed DS-Simultaneous Localization and Mapping (SLAM) accelerator is implemented on the HERO platform, which contains an Intel i5-7260U CPU as a host side and an Arria 10 GX 1150 Field Programmable Gate Array (FPGA) as an acceleration board
The semantic segmentation thread that needs to be accelerated is deployed on FPGA, and the others run on CPU
Summary
Mobile robots are widely used in fields such as industry, agriculture, medical and services [1,2], but are well used in dangerous situations such as urban security [3], national defense [4] and space detection [5,6]. With the development of artificial intelligence technology, mobile robots perform complex algorithms to complete more advanced tasks. The insufficient computing capability and limited energy of traditional computing platforms has become the bottleneck of intelligent robot development [7,8]. Mobile robots need to perceive the information of the surrounding environment but to clearly determine their position, which leads to the SLAM (Simultaneous Localization and Mapping) algorithm. Visual SLAM has some problems with the dynamic environment and the demand for advanced tasks, such as the lack of understanding about environmental information and the stability of the system in dynamic environments [9]
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