A 3D Lidar SLAM System Based on Semantic Segmentation for Rubber-Tapping Robot

Abstract

Simultaneous localization and mapping (SLAM) in rubber plantations is a challenging task for rubber-tapping robots. Due to the long-term stability of tree trunks in rubber plantations, a SLAM system based on semantic segmentation, called Se-LOAM, is proposed in this work. The 3D lidar point cloud datasets of trunks collected in rubber plantations of Hainan University are used to train the semantic model, and the model is used to extract features of trunk point clouds. After clustering the trunk point clouds, each single rubber tree instance is segmented based on the Viterbi algorithm. The point clouds of tree instances are fitted to the cylindrical trunk models for semantic cluster association and positional estimation, which are used for lidar odometry and mapping. The experimental results show that the present SLAM system is accurate in establishing online mapping, and the location of the trunk in the map is clearer. Specifically, the average relative pose error is 0.02 m, which is better than the positioning performance of LOAM and LeGO-LOAM. The average error of estimating the diameter at breast height (DBH) is 0.57 cm, and it only takes 401.4 kB to store a map of the area of approximately 500 m2, which is about 10% less than other classic methods. Therefore, Se-LOAM can meet the requirements of online mapping, providing a robust SLAM method for rubber-tapping robots.

Stability Analysis of Planetary Rotor with Variable Speed Self Rotation and Uniform Eccentric Revolution in the Rubber Tapping Machinery

“…control of intelligent rubber tapping machinery by introducing industrial ranging technology involving machine vision [18][19][20], laser ranging [21,22] and ultrasonic ranging [9]. But these technologies can only guide machines to accurately determine the position of the outer surface of the bark and cannot further quickly detect bark thickness.…”

“…In recent years, many scholars have attempted to use intelligent rubber tapping machinery, including rubber tapping robots [8][9][10][11][12][13] and fixed rubber tapping machines [14][15][16][17] instead of manual rubber tapping. They want to realize the precise tapping depth control of intelligent rubber tapping machinery by introducing industrial ranging technology involving machine vision [18][19][20], laser ranging [21,22] and ultrasonic ranging [9]. But these technologies can only guide machines to accurately determine the position of the outer surface of the bark and cannot further quickly detect bark thickness.…”