Ren, H.; Zhao, Y.; Lin, T.; Wu, J. Research on Multi-Sensor Simultaneous Localization and Mapping Technology for Complex Environment of Construction Machinery. Appl. Sci.2023, 13, 8496.
Ren, H.; Zhao, Y.; Lin, T.; Wu, J. Research on Multi-Sensor Simultaneous Localization and Mapping Technology for Complex Environment of Construction Machinery. Appl. Sci. 2023, 13, 8496.
Ren, H.; Zhao, Y.; Lin, T.; Wu, J. Research on Multi-Sensor Simultaneous Localization and Mapping Technology for Complex Environment of Construction Machinery. Appl. Sci.2023, 13, 8496.
Ren, H.; Zhao, Y.; Lin, T.; Wu, J. Research on Multi-Sensor Simultaneous Localization and Mapping Technology for Complex Environment of Construction Machinery. Appl. Sci. 2023, 13, 8496.
Abstract
Unmanned construction machinery vehicles mostly carry work in bridges, tunnels, and outdoor open spaces. Obtaining accurate pose estimation of the entire vehicle and establishing a map of the surrounding environment is of great significance for path planning and control in the later stage. Traditional simultaneous localization and mapping (SLAM) schemes, which mostly use a single sensor, but there are problems with localization drift and mapping failure in scenarios where there are few geometric features and the environment is prone to degradation. Currently, the multi-sensor fusion strategy has been proven to be an effective solution and widely used in the field of unmanned vehicle localization and mapping. This paper proposes a SLAM framework that tightly couples a LiDAR, IMU and camera to achieve accurate and reliable pose estimation. The framework is based on LiDAR-inertial system(LIS) and factor graph optimization theory. Texture information provided by vision is integrated into the LiDAR-inertial odometry to generate a new visual-inertial subsystem(VIS).The two subsystems, VIS and LIS, can assist each other and work jointly. Through real vehicle tests, the system can perform incremental, real-time state estimation, reconstruct dense 3D point cloud maps,and effectively solve the problems of localization drift and mapping failure in the lack of geometric features or challenging construction environments. Meanwhile, the system has a safety redundancy mechanism. When any subsystem fails, the system can also operate normally, to ensure the reliability and robustness of vehicle positioning.
Keywords
SLAM; multi-sensor fusion; tight coupling; factor graph optimization; construction machinery
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
