Simultaneous Localization and Mapping Algorithm Based on the Asynchronous Fusion of Laser and Vision Sensors

In this paper, a simultaneous localization and mapping algorithm based on the weighted asynchronous fusion of laser and vision sensors is proposed for an assistant robot. When compared to the synchronous fusion algorithm, this method can effectively use the redundant data in the vision sensor and improve the tracking accuracy of the algorithm. At the same time, the attitude estimation of the visual sensor is taken as a prior of the attitude estimation of the laser sensor to reduce the number of iterations and improve the efficiency of the algorithm. Further, according to the running state of the robot, a weighting coefficient based on angle is introduced to improve the confidence of the measurement. Experimental results show that the algorithm is robust and can work in a degraded environment. When compared to the synchronous fusion method, the asynchronous fusion algorithm has a more accurate prior, faster operation speed, higher pose estimation frequency, and more accurate positioning accuracy.