# Object Semantic Grid Mapping with 2D LiDAR and RGB-D Camera for Domestic Robot Navigation
## 5 Experimental Results and Discussion
### Discussion  
The statistical results of the above four scenes are shown in Table 5. The precision of the bed is 56.26%, and the recall is 100%. The precision of the couch is 80%, and the recall is 40%. Part of \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Index** & **Bed** & **Couch** & **Sink** & **Toilet** & **Microwave** & **Oven** & **Total** \\ \hline \#ground truth & 3 & 4 & 3 & 2 & 1 & 1 & 14 \\ \#instance & 4 & 1 & 1 & 1 & 1 & 4 & 12 \\ \#correct instance & 3 & 0 & 1 & 1 & 1 & 1 & 7 \\ precision (\%) & 75 & 0 & 100 & 100 & 100 & 25 & 58.33 \\ recall (\%) & 100 & 0 & 33.33 & 50 & 100 & 100 & 50 \\ \hline \hline \end{tabular} \# represents the number of.  
\end{table} 
Table 4: The statistical results of the auto scene. Figure 17: The results of the auto scene: (**a**) original point cloud, (**b**) point cloud with bounding rectangles, (**c**) the ground truth, and (**d**) the object semantic grid map. the reason is that the object detection algorithm easily misidentifies a couch as a bed, reducing the precision of the bed and the recall of the couch. The precision of the sink is 100%, and the recall is 50%. The precision of the toilet is 100%, and the recall is 66.67%. If only the sink and toilet in the bathroom are considered, the recall of them is not much different, but the system hardly instantiated the sink in the kitchen; there is a 16.67% gap between the two. The microwave is relatively small in size and can be easily removed as noise, so its precision and recall are 50% and 66.67%, respectively. In contrast, the size of the oven is relatively large, so the recall is 100%. However, the object detection algorithm recognized many other objects as an oven, so the precision is 40%. In summary, the average precision of the system is 68.42%, and the recall is 65%. The sources of error in the precision and recall mainly include the following three aspects:  
1. The object detection algorithm generates many wrong results as we directly employ an instance of Mask R-CNN pretrained with the COCO dataset. 2. There are many insufficient observations of objects, resulting in the corresponding object point clouds being sparse and removed by the point cloud filter algorithm. 3. An object is instantiated into two, as the camera interpolation method cannot refine some camera poses. For the object goal spaces, since the free grids near an object have object semantics and the navigation goal point is located on the free grid, they can help the robot select the goal conveniently. Furthermore, with the affordance and nonaffordance spaces, the robot can choose the goal socially. However, there are still two main problems with the goal space:  
1. Since the object goal space depends on the OOMBR, it may not be accurate enough. 2. The object goal space may be defined through the walls and in other rooms. The experimental results provide important ideas for the subsequent improvement of system performance:  
1. To make the object detection more accurate, we can retrain the neural network model for domestic environments. As precision is related to the number of correctly instantiated objects, we can obviously improve it. 2. To obtain sufficient observations of the objects, the robot should approach the detected objects to achieve more complete observations and generate dense object point clouds. Based on (1), we can correctly instantiate more objects and improve the recall significantly. 3. To further improve the precision and recall scores and make the object goal spaces more in-line with the actual objects, we can apply a bundle adjustment-like method over all the estimated camera poses and depth maps to improve their accuracy. 4. As the object we detect can only be located in one room, we will use a room segmentation algorithm to solve the problem that part of the goal space is located in another room.