# Object Semantic Grid Mapping with 2D LiDAR and RGB-D Camera for Domestic Robot Navigation
## 5 Experimental Results and Discussion  
### Experimental Setup  
Currently, the academic community has not established a unified standard for the evaluation of semantic maps. Although absolute and relative trajectory errors are usually used to evaluate the algorithm in the field of SLAM, the state-of-the-art can achieve centimeter accuracy in small and medium-scale scenes [44]. For domestic robot navigation, further comparison of the trajectory accuracy is of little practical significance. This evaluation method is more in-line with the needs of 3D reconstruction, virtual reality (VR), augmented reality (AR), and other fields. If we consider how humans are navigating, we can apply this approach to the robot's path planning. When humans are navigating, they usually do not need to reach the accurate goal position but only need to reach the approximate position near an object and then carry out refined operations. Similar to if the robot can navigate to an object and then use the object to establish a reference coordinate system. This approach to path planning can improve a robot's overall completion of operation tasks. Using this method may be more practical than requiring the robot to further build a very accurate map [45].  
To summarize, for the following considerations, we do not evaluate the accuracy of robots' trajectories and objects' poses: (1) the current laser SLAM algorithm can meet the navigation accuracy requirements for small and medium-scale domestic environments. (2) Unlike AR and VR application scenarios, the robot does not need very accurate object poses when performing navigation tasks.  
We use precision and the recall of object instantiation to evaluate object semantic gird maps. Supposedly the number of correctly instantiated objects, which have the correct semantic labels and their position errors are not very obvious, is \(N_{\text{cins}}\), the number of instantiated objects on the map is \(N_{\text{ins}}\), and the number of objects in the environment is \(N_{\text{gins}}\); then, the precision \(P_{\text{ins}}\) and recall \(R_{\text{ins}}\) of object instantiation are defined as follows:  
\[P_{\text{ins}}=\frac{N_{\text{cins}}}{N_{\text{ins}}}, \tag{24}\]  
\[R_{\text{ins}}=\frac{N_{\text{cins}}}{N_{\text{gins}}}. \tag{25}\]  
For object detection, we employ an instance of Mask R-CNN pretrained with the COCO [46] dataset, which can detect the six object categories used in this paper.  
For the dataset, we use Robot@Home [47] to verify the system. The dataset was generated by a Giraff robot, which was equipped with four Asus Xtion Live RGB-D cameras (manufacturer, city, country) and a Hokuyo 2D laser scanner (manufacturer, city, country). The four cameras were  
Figure 13: Object semantic gird map.  
vertically placed on the robot base, making the overall camera field of view up to \(\sim\)180\({}^{\circ}\). Considering that most robot platforms are only equipped with one RGB-D sensor, we only use the RGB-D information coming from the front. The data were collected within five dwelling apartments, named auto, alma, pare, rx2, and sarmis. As the observations from the laser scanner were saved at a lower frequency in sarmis than in the rest of the apartments, we cannot build its map successfully. Thus, we use alma, rx2, pare, and auto to verify the effectiveness of the system.