A Real-Time Hardware-Accelerator-Aided MJ-EKF SLAM Algorithm for Large-Scale Map Based on Heterogeneous Multi-Core SoC

As a classic SLAM algorithm, LIDAR-based EKF SLAM still remains a problem. An open issue is high computational complexity, which leading a very limited map size to sufficient real-time requirements. To address this problem, in this paper, we propose an MJ-EKF SLAM system that is fully implemented on a Heterogeneous Multi-Core SoC(HMS). To limit the EKF SLAM computation complexity, the Map Joining algorithm participates in the framework. The most computation cost step in EKF SLAM and Map Joining algorithms is implemented on an MJ-EKF dedicated accelerator. Meanwhile, several hardware optimization methods are proposed to save logic resources, avoid redundant computation and reduce data communication between on-chip memory and off-chip main memory. Field experiments demonstrate the HMS-based MJ-EKF algorithm achieved high real-time performance (over 30Hz) in a large-scale map with 500 landmarks.