Hardware Design of a Multi-Source Data Fusion Robot Positioning System

Design and Implementation of a Multi-Source Data Fusion Robot Positioning System

Based on a multi-source data judgment and calibration algorithm, a multi-source data fusion robot positioning system is designed. This chapter details both the hardware and software design of this system. The system utilizes satellite navigation, inertial navigation, and wheel odometry as its positioning system, ensuring real-time performance, reliability, and accuracy in complex environments. Furthermore, it achieves low system cost by avoiding expensive sensors such as LiDAR. The system also boasts excellent computational performance, capable of deploying deep learning and path planning functionalities. With good compatibility, it can be deployed in low-speed shuttles, unmanned boats, drones, and other machines, thereby achieving low-cost, high-precision autonomous driving.

The robot positioning system is designed based on a GNSS/INS/odometry fusion positioning method, thus requiring the setup of a GNSS positioning system, an inertial navigation system, and an odometry positioning system. A GNSS positioning system capable of RTK positioning technology requires three components: a satellite receiver, a differential positioning base station, and a cloud communicator. The satellite receiver is responsible for receiving satellite signals to generate GNSS positioning source data. The differential positioning base station is responsible for generating RTK differential data. The cloud communicator is responsible for sending RTK differential data to the satellite receiver.

The inertial navigation system and odometry positioning system require wheel encoders, motor drivers, and IMU modules to generate odometry positioning source data and issue pose