###### Abstract  
Simultaneous localization and mapping (SLAM), as a key task of unmanned vehicles for construction machinery, is of great significance for later path planning and control. Construction tasks in the engineering field are mostly carried out in bridges, tunnels, open fields, etc. The prominent features of these environments are high scene similarity, few geometric features, and large-scale repetitive texture information, which is prone to sensor detection degradation. This leads to positioning drift and map building failure. The traditional method of motion estimation and 3D reconstruction uses a single sensor, which lacks enough information, has poor adaptability to the environment, and cannot guarantee good positioning accuracy and robustness in complex environments. Currently, the strategy of multi-sensor fusion is proven to be an effective solution and is widely studied. This paper proposes a SLAM framework that integrates LiDAR, IMU, and camera. It tightly couples the texture information observed by camera, the geometric information scanned by LiDAR, and the measured value of IMU, allowing visual-inertial odometry (VIO) and LiDAR-inertial odometry (LIO) common implementation. The LIO subsystem extracts point cloud features and matches them with the global map. The obtained pose estimation can be used for the initialization of the VIO subsystem. The VIO system uses direct method to minimize the photometric error and IMU measurement error between images to estimate the pose of the robot and the geometric structure of the scene. The two subsystems assist each other to perform pose estimation, and can operate normally even when any subsystem fails. A factor graph is used to combine all constraints to achieve global pose optimization. Keyframe and sliding window strategies are used to ensure real-time performance. Through real-vehicle testing, the system can perform incremental and real-time state estimation and reconstruct a dense 3D point cloud map, which can effectively solve the problems of positioning drift and mapping failure in the lack of geometric features or challenging construction environments. SLAM; multi-sensor fusion; tight coupling; factor graph optimization; construction machinery +
Footnote †: journal: Medical Imaging and Image Processing  
**Acticle**  
Haoling Ren [PERSON]; [PERSON]; [PERSON]; [PERSON] Research on Multi-Sensor Simultaneous Localization and Mapping Technology for Complex Environment of Construction Machinery  
**Clation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Research on Multi-Sensor Simultaneous Localization and Mapping Technology for Complex Environment of Construction Machinery. _Appl. Sci._**2023**, _13_, 8496. [[https://doi.org/10.3309/appl3148496](https://doi.org/10.3309/appl3148496)]([https://doi.org/10.3309/appl3148496](https://doi.org/10.3309/appl3148496))  
**Data:**  
bias in pure rotation motion. Although LiDAR can capture rich environmental information from a long distance, it has poor dynamic performance and degrades in environments with few geometric features. In order to make up for the lack of a single sensor, the SLAM framework that incorporates multiple sensors have been proposed, which can combine the advantages of different sensors to better realize the pose estimation of the robot, especially in some complex environments, which can play a great role. There are SLAM systems that combine camera and LiDAR [1; 2]. LIMO combines monocular vision and LiDAR, uses LiDAR to obtain image depth information, and uses the bundle adjustment of visual keyframes to estimate the motion between keyframes. The direct visual SLAM [3] using sparse depth point cloud, because the resolution of the LiDAR is much lower than the resolution of the camera, a large number of pixels cannot obtain depth information. Later, Ref. [4] used Gauss-Newton regression to interpolate missing values and solved the problem of resolution matching.