# Autonomous Mobile Robot Navigation in Sparse LiDAR Feature Environments
## 4 The Localization Switching Method in the Structure-Less Environment
### Deep Learning-Based Corridor Recognition for Switching Localization Systems  
To avoid the corridor effect, this paper proposes to use deep learning to identify where is corridor area to switch the localization system. Because we need to know whether the current environment belong to corridor area or not, we will define the corridor recognition problem as the binary classification problem. The process will be following as below:  
1. First, to collect images that represent the current area, we need to convert the LiDAR point-cloud data into 2D images by the following formula: \[p_{pic}=r\underbrace{\left[ \begin{array}{cc}\cos{pic_{\theta}}&-\sin{pic_{ \theta}}\\ \sin{pic_{\theta}}&\cos{pic_{\theta}}\end{array} \right]}_{R_{pik}}p_{lidar}+ \underbrace{\left[ \begin{array}{c}{pic_{x}}\\ {pic_{y}}\end{array} \right]}_{t_{pic}}\] (12)  
Figure 9: The environment map constructed by 2D LiDAR; black indicates obstacles (e.g., walls); white indicates no obstacles and gray represents unknown areas. where \(p_{pic}\) is the position of the point-cloud on the picture, \(p_{lidar}\) is the position of the point-cloud on real world, \(R_{pic}\) is the transfer matrix from the LiDAR point-cloud position to the image point-cloud position and \(t_{pic}\) is the offset of the LiDAR point-cloud position from the image point-cloud position. To convert the real scale to image pixels, and we set a pixel equal to 0.05 m with \(r\) is the image resolution. The point-cloud range is set within a square of 10 m \(\times\) 10 m with the center of the mobile robot as the base, as shown in Figure 10a. Finally, the point-cloud information is drawn on the two-dimensional picture with the map coordinates \((100,\ 200)\) as the center of the mobile robot through a conversion matrix, as shown in Figure 10b. 2. When putting the 2D point-cloud image into the deep neural network for recognition, it is found that if there are people in the image, this will cause noise, and the recognition performance of the corridor is poor. Therefore, image edge detection is used to empirically determine the Region of Interests (ROI) of \(x\geq 100\) and \(90\leq y\leq 110\) in the range of the image. The ROI content then is filtered noise, as shown in Figure 11. After image preprocessing, it is put into a deep neural network to determine corridor area. 3. For the corridor recognition network, we use 2 different InceptionV3 [18] and LeNet-5 [24] architectures. Despite having impressive performance in classification tasks, most deep neural networks require powerful hardware support for their heavy computation, leading to difficulties deploying the deep learning method into edge devices such as AGV. In this paper, we choose the lightweight deep neural networks, which have a small number of parameters but still give a good performance, to implement on our system. In the Inception V3-based corridor classification model, we apply the fine-tuning approach to adopt ImageNet for speeding up the training phase and the model accuracy. Moreover, we also define a lightweight model, inspiring by LeNet. The proposed LeNet-inspired model, shown in Table 1, has fewer parameters than the InceptionV3-based model but keeps a good classification performance. 4. When a long corridor area is detected by the trained deep neural networks, the AGV avoids the mislocalization problem by switching the SLAM localization system into the IMU-based Odometer localization system.