# Object Semantic Grid Mapping with 2D LiDAR and RGB-D Camera for Domestic Robot Navigation
## 5 Experimental Results and Discussion
### Results  
The experimental results of the alma scene are shown in Figure 14, and the statistical results are presented in Table 1. The system instantiated five objects, including two beds, one couch, one sink, and one microwave. Four objects, which were two beds, one couch, and one sink, were correctly instantiated. Therefore, the precision was 80%, and the recall was 50%. For each object, the precision and recall of the bed were 100%. The precisions of the couch and the sink were 100%, and the recalls were 50%. As the system failed to instantiate the toilet and microwave, the corresponding indexes were 0%. As shown in Figure 14d, the goal spaces of the objects are generated. Since the free grids near the objects have object semantics, and the navigation goal point is located on a free grid, they can facilitate \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 & 2 & 2 & 2 & 1 & 1 & 0 & 8 \\ \#instance & 2 & 1 & 1 & 0 & 1 & 0 & 5 \\ \#correct instance & 2 & 1 & 1 & 0 & 0 & 0 & 4 \\ precision (\%) & 100 & 100 & 100 & - & 0 & - & 80 \\ recall (\%) & 100 & 50 & 50 & 0 & 0 & - & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The statistical results of the alma scene. Figure 14: The results of the alma scene: (**a**) original point cloud, (**b**) point cloud with bounding rectangles, (**c**) the ground truth, and (**d**) the object semantic grid map. the robot to select the goal point. In addition, due to the consideration of the affordance of the object, the goal point selection can be socialized to a certain extent. The experimental results of the rx2 scene are shown in Figure 15, and the statistical results are shown in Table 2. The precision is 66.67%, and the recall is 80%. Specifically, the system instantiated six objects, including three beds, one couch, one sink, and one toilet. Among them, four objects were correctly instantiated--namely, one bed, one couch, one sink, and one toilet. For each object, although the recall of the bed was 100%, the object detection algorithm recognized some observations that belonged to the couch as a bed, so two beds were incorrectly instantiated, resulting in a precision of 33.33%. The precisions and recalls of the couch and toilet were 100%. The precision of the sink was 100%, and the recall was 50%. The reason is that the scene contains two sinks, but one of them is far from the observation position of the robot and cannot be instantiated correctly, so its recall is lower than precision. In addition, as shown in Figure 15d, each object instance has generated the object goal space, which can help the robot select navigation goals. The experimental results of the pare scene are shown in Figure 16, and the statistical results are shown in Table 3. The precision is 73.33%, and the recall is 84.62%. The system instantiated 15 objects, including seven beds, two couches, two sinks, two toilets, one microwave, and one oven. \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 & 1 & 1 & 2 & 1 & 0 & 0 & 5 \\ \#instance & 3 & 1 & 1 & 1 & 0 & 0 & 6 \\ \#correct instance & 1 & 1 & 1 & 1 & 0 & 0 & 4 \\ precision (\%) & 33.33 & 100 & 100 & 100 & - & - & 66.67 \\ recall (\%) & 100 & 100 & 50 & 100 & - & - & 80 \\ \hline \hline \end{tabular} \# represents the number of.  
\end{table} 
Table 2: The statistical results of the rx2 scene. Figure 15: The results of the rx2 scene: (**a**) original point cloud, (**b**) point cloud with bounding rectangles, (**c**) the ground truth, and (**d**) the object semantic grid map. Among them, 11 objects were instantiated correctly, including three beds, two couches, two sinks, two toilets, one microwave, and one oven. The bed recall is 100%, and precision is 42.86%.