## 3 Long-Term Feature Points Localization
### Adaptive Contact Points Tracking
However, under continuous outdoor operation, the baseline method faces two main problems: It is difficult to ensure stable positioning under complicated light conditions based on grayscale matching positioning; (A near-infrared spectrum sensor can assist to locate the target in a robust manner according to the contact heat. However, this kind of sensor is difficult to become a conventional solution in terms of resolution, frame rate, and price.) the judgment of the tracking effect is delayed, resulting in slow abnormality tracking, and the similar target produced by the line change will cause the tracking judgment to become inaccurate. Parameter adaptive localization: In response to this phenomenon, we propose a parameter adaptive tracking method based on the baseline method. Compared with the tracking effect judgment that depends solely on the tracker itself, we define a tracking effect verification module based on the topology information of the contact points, so that the tracker will provide the tracking position and tracking effect simultaneously, and this  
Figure 2: Contact points localization by template matching. Two template signals are shown in the bottom-right corner in bright pink. effect will be used to feedback and optimize the tracking parameters, which then constructs a closed-loop tracking framework. The tracking effect mainly comes from two discrimination models. L1 discrimination is based on location and hash similarity discrimination in the patch. L1 discrimination is generally used to verify tracking failures originating from similar targets. We can take the positioning results in the image from the left camera as an example, in which the energy of L1 is as follows:  
\[E_{l1}=L\Big{(}p_{l}^{i},p_{r}^{i}\Big{)}+\beta\Big{(}L\Big{(}p_{l}^{i},p_{l}^{ i}\Big{)}+L\Big{(}p_{r}^{i},p_{r}^{i}\Big{)}\Big{)}, \tag{6}\]  
where \(p_{l}^{i},p_{r}^{i}\) are the two image contact points; \(p_{l}^{i},p_{r}^{i}\) are the corresponding polar lines obtained from the corresponding points in the right image in the processing of epipolar rectification; \(L\) is used to calculate the norm of two points, or the distance from the point to the polar line, and then calculate the loss according to the distribution interval of the output value; and \(\beta\) is used in the interlaced verification of stereo camera images and is set to zero when only verifying one image. In terms of judgment after continuous tracking, it is not robust to rely solely on the tracker itself. We used hash similarity to improve the discrimination accuracy. As the contact line changes, the tracker cannot achieve autonomous switching, and \(E_{l1}\) after the corner approach is also used. First, as the relative position of the target remains approximately constant between consecutive frames, the contact point coordinates after tracking can be obtained by the following formula:  
\[p_{l}^{i},p_{r}^{i} =\phi_{inverse-RP}(\text{ROI}^{i},\text{shift}^{i}), \tag{7}\] \[p_{l}^{i},p_{r}^{i} =\phi_{\text{refine}}\Big{(}p_{l}^{i},p_{r}^{i}\Big{)}, \tag{8}\]  
where ROI\({}^{i}\) is the tracking area under the current frame and shift\({}^{i}\) is the relative displacement saved in the previous RP. Then, the real contact position is obtained by corner approximation. After that, as shown in Figure 3, the image blocks corresponding to the left and right contact points are compared by p-hash and the similarity is calculated as follows:  
\[K_{l}^{i},K_{r}^{i}=\phi_{patch}\Big{(}p_{l}^{i},p_{r}^{i}\Big{)}, \tag{9}\]  
\[p_{l}^{i},p_{r}^{i} =\phi_{Cross}(\{p_{l}\},\{p_{r}\},\{p_{c}\}), \tag{2}\]0  
where \(K_{l}^{i},K_{r}^{i}\) is the corresponding image block generated by the left and right contact points in the \(i\)-th frame image, respectively, and \(\phi_{patch}\) is the image block generation mode. In practice, a 32 \(\times\) 32 image slice is selected with the contact point as the center to obtain the similarity of the input images after hash coding.