## 6 Experimental Results and Discussion
### Dataset  
The dataset was recorded from the automatic forklift platform in a classic warehouse. Two identical grayscale cameras were used to capture the images. The specific model and parameters were as follows: FLIR Flea3 FL3-U3-13E4M-C mono camera, 1.3 megapixels, 1/1.8\" e2v EV76C560 CMOS, global shutter, and maximum resolution 1280 \(\times\) 1024; each camera was equipped with a Kowa lens with a 5 mm focal length, F2.8-F16, and \(71.7^{\circ}\times 57.1^{\circ}\) angle of view. The baseline of the two cameras was set to 30 cm. The left and right cameras were triggered by the same source generated by STM32F103 MCU in order to synchronize their images, and the image acquisition frequency was set to 20 Hz.  
The ground truth of the forklift needed to be recorded to evaluate the performance of the SLAM algorithm. The general practice was to use a motion capture system such as VICON to obtain the motion truth of the robot through external cameras and achieve high accuracy. However, the deployment of a motion capture system in a typical warehouse is very expensive because of its large area. In contrast to a UAV, the forklift only moved on the ground, so its position and heading could be used to describe the ground truth of the forklift. SICK LiDAR was deployed on top of the forklift to obtain the ground truth of the forklift by measuring a reflective landmark that was pre-arranged in the warehouse. The LiDAR model was NAV350; the scanning frequency was 8 Hz; and the scanning angle was 360\({}^{\circ}\). The cameras and SICK LiDAR are shown in Figure 4.  
The cameras were connected to the PC through USB 3.0 interfaces, and the LiDAR was connected to the PC through a Gigabit Ethernet interface run at 100 megabits. The recording PC ran on Intel I5 6200U, 8 GB RAM, and 256 GB SSD. The PC ran the Ubuntu 18.04 64 bit system. In order to prevent frame loss and ensure complete data recording, we used optimized multithreading to process each camera and the LiDAR, respectively. The grayscale images were stored in the 8 bit PNG image format, and the ground truth recorded by LiDAR was recorded in a TXT file.  
Because the wide angle lens was used, it was necessary to calibrate the cameras. After the dataset collection, the radial distortion and stereo rectification were implemented. Sample rectified images from the dataset are shown in Figure 5. This was a challenging dataset since its area was significantly larger than that of an ordinary indoor scene: the length of the warehouse was 100 m, and the width is 30 m. Meanwhile, the warehouse had dynamic illumination, long corridors, and repetitive scenes.  
Figure 4: Dataset collection platform that includes the stereo camera, NAV350 laser sensor, artificial reflective landmark, and automatic guided forklift. (**a**) Stereo camera. Two monocular cameras triggered by the same signal source were used to capture pictures of the warehouse. (**b**) SICK NAV350. The NAV350 sensor was used to measure the global position of the forklift in the warehouse. (**c**) The forklift running in the warehouse.  
Figure 5: Sample images from the warehouse dataset, which contains the typical scene in the warehouse and these images from the left camera.