Multi-Sensor Fusion Tomato Picking Robot Localization and Mapping Research

Abstract

Abstract To address the current problem of unsatisfactory localization and map building of an intelligent picking robot mobile platform in an unstructured picking environment, a multi-sensor fusion localization and map building method is proposed to fuse the visual odometer position estimation results with IMU and wheel odometer. And the pre-fusion processing of wheel odometer data is proposed to solve the problem of large errors of wheel odometers on uneven ground. The multi-sensor fusion scheme is proposed to fuse vision and laser SLAM methods to build a more information-rich point cloud and raster fusion map for the problem of non-closure caused by sparse branches. The experimental results show that the multi-sensor fusion SLAM method can obtain more accurate, richer, and more robust maps.