High-precision simultaneous localization and mapping (SLAM) is one of the core technologies of unmanned driving. LiDAR-based SLAM algorithms are often complex and computationally intensive, and usually are deployed on high performance CPU or GPU computing architecture with high power consumption and low energy efficiency ratio, which is not conducive to vehicle-level applications. In this paper, we design and implement a low power CPU and FPGA hybrid computing architecture for accelerating the key algorithm of LiDAR-based localization scheme. More specifically, we propose a software and hardware co-design strategy: (1) we first propose chain representation as a new type of map representation, which uses the depth discontinuity region as the segmentation location to segment the point cloud data. Our method not only reduces noise issues for down-sampling operation in point cloud representation, but also has the same computational and storage overhead as point cloud representation. (2) We further exploit the inherent parallelism in the algorithms to design a pipeline hardware architecture, which can effectively improve the speed of the algorithm in the embedded platform. Deployed on the Xilinx ZCU102 platform, our system achieves 24.4x and 3.2x speedups compared to the ARM Cortex A53 processor and the Intel i7-10700 processor, respectively, at 4.204W power consumption without severely degrading the final output quality.
Design Hybrid Computing Architecture for Accelerating Point Cloud Registration
2023-06-04
1488237 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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