SoC Implementation of Visual-inertial Odometry for Low-cost Ground Robots

Abstract

Simultaneous localization and mapping (SLAM) is a key technique for autonomous navigation of a robot in unknown environments. Recently, many researchers focus on the algorithm optimization of visual-inertial odometry (VIO) as a real-Time SLAM method. In this paper, different from pure algorithm research, we propose a novel low-cost hardware solution based on the system on a chip (SoC) to achieve real-Time performance and low power consumption of VIO. We design a novel hardware architecture to accelerate a popular VIO algorithm, with the time-costly image feature processing computed on FPGA and floating-point numerical computation been done on ARM. On the algorithm side, we fuse odometry information from the two wheels of the ground robot in a unified optimization method, which results in accurate metric scale estimation of the VIO. The experimental results show that our design and implementation have superior performance for ground robot applications, allowing real-Time localization of a robot with a very low-cost hardware for computation in complex environments.