## 3 Experimental Results and Analysis
### Stereo-Matching Experiments  
We used the stereo camera platform to take pictures of random indoor scenes, and each image effect was arranged as an overexposure effect using light exposure. The pairs of images obtained were used to experimentally compare the currently commonly used feature detection and feature matching algorithms. The experimental results are shown in Tables 1 and 2. Figure 7: GVDS algorithm flow chart. Table 1 compares the performances of the commonly used feature detection algorithms, where FAST-50 and FAST-40 indicated that the threshold value was set to 50 and 40 when deploying the FAST detection algorithm, respectively, and SAD-FAST-\(t\) indicated that the threshold value was a self-adaptive threshold \(t\) when deploying the SAD-FAST detection algorithm. As can be seen from Table 1, the FAST series feature detection algorithms not only had much lower computation times than the seven other algorithms when performing feature detection for any scene but also detected more feature points than the other algorithms. Furthermore, the use of our proposed self-adaptive-threshold SAD-FAST detection algorithm showed that the improved algorithm was significantly more adaptable and could detect 70% and 45.1% more feature points than FAST-50 and FAST-40, respectively. Figure 9 shows the experimental results for the feature matching with the FLANN matching algorithm and the Mahalanobis distance determination after feature extraction with several feature extraction algorithms. Figure 9a-e show the result plots for the ORB algorithm, SURF algorithm, SIFT algorithm, AKAZE algorithm, and BRISK algorithm after extracting the image features and then using FLANN matching, respectively. Figure 9f-j \begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Number of Feature Points**} & \multicolumn{1}{c}{**Time Consumption for**} \\
**Detection Algorithms** & **Detected by Image** & \multicolumn{1}{c}{**Feature Point Detection/ms**} \\  & **Pair/Points** & \\ \hline KAZE [53] & 275 & 141 \\ AKAZE [54] & 222 & 120 \\ BRISK [55] & 311 & 276 \\ FREAK [56] & 98 & 56 \\ SIFT [57] & 406 & 952 \\ SURF [58] & 601 & 174 \\ BRIEF [59] & 98 & 92 \\ FAST-50 [60] & 514 & 19 \\ FAST-40 & 602 & 27 \\ SAD-FAST-\(t\) & 874 & 32 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison of feature detection algorithms. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Algorithms** & **Matching Success Rate/\%** & **Matching Time/ms** \\ \hline ORB [61] & 64 & 36.6 \\ SIFT [57] & 87.7 & 156.7 \\ SURF [58] & 79.4 & 312.1 \\ AKAZE [54] & 71.6 & 41.7 \\ BRISK [55] & 75.2 & 46.3 \\ ORB + SURF & 81.4 & 73.2 \\ AKAZE + SURF & 82.1 & 149.4 \\ SIFT + SURF & 76.7 & 243.9 \\ BRISK + SURF & 79.6 & 376.1 \\ KAZE + SURF & 81.9 & 69.4 \\ SAD-FAST-\(t\) + SURF & 85.1 & 57.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of several feature extraction algorithms using FLANN matching. Figure 8: Experimental platform with stereo camera. show the results of feature matching using FLANN for five types of \"disorganized\" stereo-matching systems; namely, ORB + SURF, AKAZE + SURF, SIFT + SURF, BRISK + SURF, and KAZE + SURF. Figure 9k shows a result graph for the use of the SAD-FAST algorithm for feature detection, the SURF feature description algorithm for the obtained feature points, and, finally, the FLANN matching algorithm for matching. Table 2 shows the matching performances of several corresponding feature extraction algorithms. From Table 2, we can see that our proposed SAD-FAST feature detection + SURF feature description + FLANN matching algorithm, in a combination of stereo-matching experiments, was slightly slower than the ORB algorithm, AKAZE algorithm, and BRISK algorithm at matching in real time when dealing with feature matching in scenes strongly affected by illumination, but the number of feature-point pairs and the matching success rate were much higher than for these three algorithms.