Electronic Sensor Multi-Modal Slam Algorithm Based on Information Fusion Technology

Abstract

When the multi-mode electronic sensor based on information fusion works, it will be interfered by various noises in the process of data acquisition and transmission, and the signal will appear nonlinear distortion. Therefore, this paper needs to compensate the input analog quantity to a certain extent. In this paper, multiple sensors such as lidar and camera are used to obtain environmental information. These sensors measure the environment, resulting in a series of data points. These data points are input into the information fusion system for processing to obtain a high-precision environment model. The information fusion system uses the SLAM method to estimate and correct the measurement results of each sensor, so as to obtain a more accurate environmental model. This paper describes the sensor and the algorithm used in it, and obtained the data through case analysis and multi-modal experiments. The experimental results show that the error of the multi-modal SLAM algorithm is reduced to 1.1 %, and the accuracy is 98.9%. This has a great effect on the reconstruction variable and parameter extraction accuracy in the target environment.