Aiming at the risk of insufficient positioning accuracy and stability of autonomous mobile weeding robots relying on a single sensor when deployed in complex hilly and mountainous photovoltaic power station scenarios, this paper aims to propose a multi-sensor fusion localization method based on factor graph optimization.
By using pose constraints obtained from three types of sensor data, namely, a visual odometry combining point and line features, Inertial Measurement Units (IMU) pre-integration and satellite Real-Time Kinematic (RTK) signals, the visual point-line fusion residual, IMU residual and satellite RTK residual are, respectively, derived and incorporated into a multifactor graph model. The fusion localization system takes visual keyframes as the reference, synchronizing and tightly coupling multi-sensor information into a nonlinear least squares estimation problem to achieve optimal estimation of the robot’s global pose.
The proposed multi-sensor fusion localization method has been validated on the public Rosario data set, demonstrating favorable accuracy and robustness even under degraded satellite signals. This provides a novel and reliable solution for enhancing the positioning accuracy and stability of weeding robots in complex operating scenarios.
This study fuses the exploitable line features in the complex environments of weeding robots into a point feature-based visual odometry. Through the factor graph optimization framework, it integrates visual point-line odometry, IMU pre-integration and satellite RTK data, designing a tight coupling strategy based on visual keyframes and efficient, robust hard-threshold time synchronization.
