# Tracking and Registration Technology Based on Panoramic Cameras
## 4 Experiments
### Comparison Results  
The proposed method is compared with the traditional approach, OpenVSLAM [13], and two other unsupervised methods, 360-SelfNet [18] and BiFuse++ [22]. The comparison includes both the estimation results of scene depth and the estimation accuracy of camera pose parameters. **Comparison of Scene Depth Estimation Results. ** To verify the effectiveness of the proposed method in scene depth estimation, the model is evaluated on the test set de rived from the PanoSUNCG dataset, which consists of panoramic image sequences from 23 different scenes. Since the traditional method, OpenVSLAM, reconstructs a sparse map based on feature point matching, it can only generate sparse point clouds. In contrast, the proposed method, along with 360-SelfNet and BiFuse++, can generate dense depth maps. Therefore, a direct comparison with OpenVSLAM in terms of scene depth information is not applicable. Figure 5 presents the visual comparison of depth estimation results across different methods. Each row corresponds to the same input image, while each column shows the depth maps estimated by 360-SelfNet, BiFuse++, and the proposed method, as well as the ground truth depth map. As shown in Figure 5, noticeable differences can be observed among the results of different depth estimation methods. The 360-SelfNet method estimates depth on cubemap panoramic images, which leads to discontinuities in the generated depth maps. In contrast, BiFuse++ addresses image distortion by fusing features from both cubemap and equirectangular panoramas and directly predicts depth on equirectangular panoramas, resulting in improved estimation quality. The proposed method employs rectangular convolution filters at multiple scales to handle the distortion in equirectangular panoramic images, achieving depth estimation results comparable to BiFuse++ and significantly better than 360-SelfNet. To comprehensively validate the effectiveness of panoramic depth estimation, the experiment adopts the four error evaluation metrics and three accuracy evaluation metrics described in Section 4.1 as quantitative indicators of prediction accuracy. Lower values in error metrics indicate more precise estimations, while higher values in accuracy metrics represent more reliable results. From Table 4, it can be observed that the BiFuse++ method achieves the best overall performance, while the proposed method yields the second-best results. This is because BiFuse++ employs a depth estimation network that fuses features from both equirectangular and cubemap panoramas, providing a more effective solution to panoramic image distortion than the proposed method. Consequently, BiFuse++ outperforms our method in terms of accuracy. In contrast, 360-SelfNet estimates depth based on cubemap panoramas using a network originally designed for perspective images. While this approach avoids handling distortion in equirectangular panoramas, it suffers from discontinuities at the cube face boundaries, which negatively impact the accuracy of the estimated depth maps. Comparatively, the proposed method applies multi-scale rectangular convolution modules  
Figure 5: Comparison of panoramic depth estimation visualization results. to address distortion, leverages dilated convolutions to expand the receptive field, and directly estimates depth on equirectangular panoramic images. These strategies enable our method to outperform 360-SelfNet in depth prediction accuracy. **Comparison of Camera Pose Estimation Results**. To thoroughly validate the effectiveness of panoramic camera pose estimation, the experiment adopts the Relative Pose Error (RPE) as the evaluation metric. As shown in Table 5, the traditional method, OpenVSLAM, achieves the best results (bolded), while the deep learning-based BiFuse++ method ranks second (underlined). The proposed method yields PRE-R and RPE-T values that are 0.13\({}^{\circ}\) and 0.001 m higher than those of BiFuse++, but 0.10\({}^{\circ}\) and 0.002 m lower than those of 360-SelfNet, respectively.