## 6 Conclusions  
This paper proposes a multi-sensor fusion SLAM system for the complex environment of construction machinery, which tightly couples the texture information detected by the camera, the geometric information scanned by the LiDAR, and the measurement data of the IMU to jointly estimate the pose of the robot. At the front end of the LIO subsystem, the popular feature-based method is adopted, by extracting the edge features and plane features of the point cloud to achieve frame-to-map matching, combined with the measurements of the IMU to perform LiDAR-inertial odometry, its estimated value can also be used for initialization of the VIO system. On the front end of visual-inertial odometry, the direct method is adopted to perform visual odometry by minimizing the photometric error between image frames and combining the measurement data of IMU. The whole system incorporates VIO constraints, LIO constraints, IMU preintegration constraints, and loop closure constraints into the factor graph to achieve global pose optimization and map construction. The proposed method can run in real time on the embedded system. Verified by real vehicle experiments, which can achieve good positioning and mapping in indoor corridors and large open scenes. Using the direct method at the front end of the visual odometry can handle the data association problem in a more holistic way, which has great advantages in environments with a large number of repeated textures, and can better solve the problems of positioning drift and mapping failure of construction machinery when it is working in a complex environment with missing features. The public dataset M2 DGR was used to further validate the proposed method, comparing it with Lego-LOAM and LIO-SAM. The results show that the absolute pose error of proposed method was reduced by 4.6% and 24%, respectively, and the relative pose error was reduced by 48% and 0.3%, respectively, which also shows that the system has good positioning robustness. The system also has potential drawbacks, although the visual odometry uses a direct method and does not require feature extraction, the motion estimation is based on the assumption of constant gray level, which is easy to make the assumption invalid due to the change of camera exposure. In addition, \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{6}{c}{**RPE**} \\ \cline{2-8}  & **Max** & **Mean** & **Median** & **Min** & **Rmse** & **See** & Std \\ \hline ALOAM & 0.609 & 0.252 & 0.240 & 0.002 & 0.294 & 75.526 & 0.152 \\ Lego-LOAM & 0.909 & 0.370 & 0.364 & 0.004 & 0.412 & 72.903 & 0.182 \\ LIO-SAM & 0.806 & 0.256 & 0.262 & 0.001 & 0.279 & 66.935 & 0.111 \\ this paper & 0.561 & 0.255 & 0.265 & 0.001 & 0.278 & 66.434 & 0.110 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: RPE values for different methods. Figure 12: Histogram of positioning error of different methods in M2 DGR dataset. (**a**) Histogram of ATE by different methods. (**b**) Histogram of RPE by different methods. if a better initial estimate and image quality for vision can be provided by other methods, the accuracy and stability of the estimate can be further improved. Investigation, J.W. ; Methodology, Y.Z. ; Resources, T.L. ; Software, Y.Z. ; Validation, Y.Z. ; Writing--original draft, Y.Z. ; Writing--review and editing, H.R. and J.W. All authors have read and agreed to the published version of the manuscript. The authors declare that this study received funding from Fujian University industry university research joint innovation project plan (Grant NO. 2022H6007), Industry Cooperation of Major Science and Technology Project of Fujian Province (Grant NO. 2022H6028), and Xiamen Major Science and Technology Plan Projects (Grant NO. 3502Z20231013). The fund was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication. Not applicable. The data are not publicly available due to privacy or ethical restrictions.