Research on Mobile Robot Navigation Method Based on Semantic Information

Author:

Sun Ruo-Huai123ORCID,Zhao Xue4,Wu Cheng-Dong13,Zhang Lei23,Zhao Bin123ORCID

Affiliation:

1. College of Information Science and Engineering, Northeastern University, Shenyang 110819, China

2. SIASUN Robot & Automation Co., Ltd., Shenyang 110168, China

3. Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110169, China

4. Daniel L. Goodwin College of Business, Benedict University, Chicago, IL 60601, USA

Abstract

This paper proposes a solution to the problem of mobile robot navigation and trajectory interpolation in dynamic environments with large scenes. The solution combines a semantic laser SLAM system that utilizes deep learning and a trajectory interpolation algorithm. The paper first introduces some open-source laser SLAM algorithms and then elaborates in detail on the general framework of the SLAM system used in this paper. Second, the concept of voxels is introduced into the occupation probability map to enhance the ability of local voxel maps to represent dynamic objects. Then, in this paper, we propose a PointNet++ point cloud semantic segmentation network combined with deep learning algorithms to extract deep features of dynamic point clouds in large scenes and output semantic information of points on static objects. A descriptor of the global environment is generated based on its semantic information. Closed-loop completion of global map optimization is performed to reduce cumulative error. Finally, T-trajectory interpolation is utilized to ensure the motion performance of the robot and improve the smooth stability of the robot trajectory. The experimental results indicate that the combination of the semantic laser SLAM system with deep learning and the trajectory interpolation algorithm proposed in this paper yields better graph-building and loop-closure effects in large scenes at SIASUN large scene campus. The use of T-trajectory interpolation ensures vibration-free and stable transitions between target points.

Funder

Science and Technology Innovation 2030-“New Generation Artificial Intelligence” Major Project

Publisher

MDPI AG

Reference13 articles.

1. Visual-inertial SLAM in featureless environments on lunar surface;Xie;Acta Aeronaut. Astronaut. Sin.,2021

2. DE-SLAM: SLAM for highly dynamic environment;Xing;J. Field Robot.,2022

3. eil-slam: Depth-enhanced edge-based infrared-lidar slam;Chen;J. Field Robot.,2022

4. Heterogeneous collaborative SLAM based on fisheye and RGBD cameras;Zhang;Acta Aeronaut. Astronaut. Sin.,2023

5. Tightly coupled LiDAR SLAM method for unknown environment;Li;Infrared Laser Eng.,2023

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