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RIA-CSM: A Real-Time Impact-Aware Correlative Scan Matching Using Heterogeneous Multi-Core SoC

In dynamic scenarios with a large flow of people, robots are extremely vulnerable to impacts. Conventional correlative scan matching (CSM) is imperfect. The algorithm obtains incorrect priors caused by impact. Meanwhile, extremely high computational complexity remains a challenging issue. In this paper, we propose a computationally efficient and robust loosely-coupled LIDAR-IMU-wheel fusion method named RIA-CSM, using heterogeneous multi-core SoC. The proposed impact-aware sensor integration module provides robust priors for CSM. Instead of occupancy grids, truncated signed distance functions (TSDFs) are used to represent a map. The most time-consuming part of CSM is mapped to an FPGA-based CSM accelerator, while the remaining part is calculated by multi-core CPU. The public data set is used to test the localization accuracy and real-time performance of the proposed RIA-CSM, which shows that the real-time performance of conventional CSM has been improved by 30 times. When the robot is impacted, experimental results on a mobile robot platform are qualitatively analyzed. Compared with the state of art, our method realizes more robust localization.