# A Semantic Information-Based Optimized vSLAM in Indoor Dynamic Environments
## 4 Experiments and Evaluation  
### Experimental Data and Evaluation Criteria  
The feasibility of the proposed method was verified by testing the TUM RGB-D dataset and real scenarios using Ubuntu 18.04 on a computer (i7-9700K CPU, 16 GB RAM and Nvidia GeForce RTX 2060 GPU). The TUM RGB-D dataset consists of colour and depth images (640 \(\times\) 480) acquired by a Microsoft Kinect sensor at a full frame rate (30 Hz). Among them, the ground-truth trajectory information of the sensor is acquired by a high-precision motion-capture system with eight high-speed tracking cameras (100 Hz). This high precision motion-capture system acquires frame-by-frame relative errors in the ground truth data which are less than the 1 mm and 0.5 deg measured by the Kinect optical centre, and the absolute error of the entire motion-capture area is less than 10 mm and 0.5 deg [37]. Finally, the five indoor dynamic scenes were selected for testing in the dataset, namely freiburg3_sitting_static, freiburg3_walking_halfsphere, freiburg3_walking_rpy, freiburg3_walking_static, and freiburg3_walking_xyz. The real scenario used Intel RealSense D435 to collect the data and the device parameters are shown in Table 2.  
In order to verify the experimental results, the results of the experiments are evaluated by the following two criteria: absolute trajectory error (ATE) and relative pose error (RPE). ATE is defined as the error between the true trajectory and the estimated trajectory of the SLAM, calculated by Equation (6). RPE is defined as the error between the true position transformation and the position transformation estimated by SLAM within a certain period of time, calculated by Equation (7).  
\[\begin{split} e_{i}=||log\Big{(}T_{gt,i}^{-1}T_{est,i}\Big{)}^{ \vee}||_{2}\\ \text{RMSE}_{ATE}=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}e_{i}^{2} }\end{split} \tag{6}\]  
\[\begin{split} f_{i}=||log\Big{(}\Big{(}\Big{(}T_{gt,i}^{-1}T_{ gt,i+\Delta t}\Big{)}\Big{)}^{-1}\Big{(}T_{est,i}^{-1}T_{est,i+\Delta t}\Big{)} \Big{)}^{\vee}||_{2}\\ \text{RMSE}_{ RPE}=\sqrt{\frac{1}{N-\Delta t}\sum\limits_{i=1}^{N- \Delta t}f_{i}^{2}}\end{split} \tag{7}\]  
where \(T_{gt,i}\). represents the true pose of the \(i\)-th frame. \(T_{est,i}\) represents the SLAM estimate of the \(i\)-th frame. \(e_{i}\) represents the absolute trajectory error of the \(i\)-th frame. \(T_{est,i+\Delta t}\) represents the true pose of the \(i\)-th frame to \(i+\Delta t\)-th frame. \(T_{est,i+\Delta t}\) represents the SLAM estimate of the \(i\)-th frame to \(i+\Delta t\)-th frame. \(f_{i}\) represents the relative positional error of the \(i\)-th frame to \(i+\Delta t\)-th frame.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sensor Metrics** & **Configuration Parameters** \\ \hline Dimensions & 90 mm \(\times\) 25 mm \(\times\) 25 mm \\ Depth detection method & Active IR Stereo \\ Depth Resolution & Up to 1280 \(\times\) 720 \\ Depth Frame Rate & Up to 90 FPS \\ Depth Field of View & 91.2\({}^{\circ}\)\(\times\) 65.5\({}^{\circ}\)\(\times\) 100.6\({}^{\circ}\) \\ Depth Detection Range & 0.3–10 m \\ RGB Resolution & 1920 \(\times\) 1080 \\ RBG Frame Rate & 30 FPS \\ RGB Field of View & 69.4\({}^{\circ}\)\(\times\) 42.5\({}^{\circ}\)\(\times\) 77\({}^{\circ}\) \\ Image Sensor Type & global shutter \\ Data Transfer Interface & USB 3.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specific parameters of RealSense D43 depth camera.