## 7 Conclusions  
In this paper, we presented a stereo visual SLAM approach for a forklift operating in a large scale indoor warehouse. Experimental results showed that our method had impressive efficiency and high accuracy. The hybrid tracking model took advantage of the high efficiency of the direct method and the high precision of the indirect method. For a new frame, direct image alignment was applied to obtain its pose, and then, feature extraction and matching were implemented to update the pose of the keyframe, thereby achieving a balance between efficiency and accuracy.  
In the front-end of SLAM, visual odometry, we proposed a two stage dynamic threshold feature extraction method, which could significantly decrease the time required for feature extraction while maintaining the same quality of the keypoints. In addition, since the SLAM applied to a vehicle differed from that used for a hand held camera, the motion of the camera was constrained by the kinematics of the vehicle: therefore, a kinematic model of the forklift was constructed to provide a more reasonable initial position and pose value for the direct method and further increase the speed of optimization convergence.  
Compared with the indirect method, given the same parameters, our algorithm significantly improved the tracking efficiency on a dataset of a warehouse, and the accuracies of the two methods were comparable. Our method has great potential to be transplanted to embedded computers, such as Intel Upboard or Raspberry Pi 4.  
Although the proposed method achieved high computational efficiency and accuracy, there are still many improvements to explore in the future. First, compared with the current indirect estimation of the vehicle motion state through pose, wheel odometry can calculate the trajectory of a vehicle accurately in a short time; moreover, IMUs can measure vehicle acceleration and angular velocity more directly, and integrating IMUs or wheel odometry data can increase the robustness of pose tracking.  
Figure 11: 3D point cloud map and octree map created for the warehouse. (**a**) Point cloud map; points in red color represent the keypoints in current optimization window; black represents the optimized keypoints; blue represents keyframes; and green is the edge between keyframes. (**b**) Octree map; the different color was set according to the height of voxels.  
Then, the vehicle control and SLAM process can be combined to develop an active SLAM algorithm to enhance the adaptability of the algorithm to the environment.  
[PERSON] designed the entire core architecture. [PERSON] performed the algorithm and wrote the paper. [PERSON] supervised the research. [PERSON] reviewed and edited the manuscript. [PERSON] analyzed the data. All authors read and agreed to the published version of the manuscript.  
This research was supported, in part, by the Natural Science Fund of China (Project No. 31971806 and No. 51705161), the Sub-task of the National Key Research and Development Plan of China (Project No. 2018 YFD0701002), the Sub-task of the National Science and Technology Support Plan of China (Project No. 2015 BAD18B0301), and the Key Research and Development Plan of Guangdong (Project No. 2019B020225001).  
We are thankful to Guangdong Tianhe Storage Co., Ltd, for providing the test scenario. The authors also thank the anonymous reviewers for their critical comments and suggestions to improve the manuscript.  
The authors declare no conflict of interest.