## 6 Conclusions  
In this work, a proof of concept of a hybrid visual odometry solution has been presented. This solution combines optical flow and direct methods, and it was designed to be used with a single downward-facing, monocular camera on low-texture surfaces in dynamic, indoor public spaces. The proposed solution offers several key advantages in comparison with other pose-estimation methods, like being immune to environmental conditions that may affect external sensors and being robust with foot traffic and dynamic objects in the environment. In addition, the system maintains high precision by providing a solution that mitigates drift issues present in other odometry techniques. Furthermore, the system provides a mechanism to identify landmarks and special areas with minimal installation effort and without changes in programming.  
To achieve this result, the approach begins with the extraction and matching of image features using the Lucas-Kanade algorithm, followed by motion estimation, minimizing the error of the reprojection of the detected features across frames, and a filtering process to mitigate errors. Finally, sub-pixel accuracy is achieved by employing a template-matching algorithm that minimizes the photometric error between consecutive frames. The experimental results demonstrate the robustness of the algorithm on various surfaces, includingtile-like patterns common in indoor environments in which the system is designed to operate. Furthermore, it has been shown how the method is able to maintain a low drift error.  
The proposed method was tested on a system built as a demonstration platform designed to prove the feasibility of the method and check that it achieves the desired objective correctly. In subsequent iterations, we plan to address the low frame rate by using a faster and more optimal camera and conducting the implementation in C instead of using Python. We also believe the sub-pixel method could be further improved. Future work will include the improvement of the setup by incorporating a higher-resolution camera, which is expected to increase the accuracy of the system. Additionally, further work will include the integration of the system into an omnidirectional robot, which will facilitate extensive testing in real-world scenarios, providing further validation of the performance of the algorithm.  
Concept formulation, B.M.A.-H. and C.P.; methodology, B.M.A.-H., D.T. and C.P.; software development, D.T. and C.P.; validation, B.M.A.-H., D.T. and C.P.; formal analysis, B.M.A.-H., D.T. and C.P.; investigation, B.M.A.-H., D.T. and C.P.; resources, B.M.A.-H. and C.P.; original draft preparation, D.T.; review and editing, B.M.A.-H. and C.P.; visualization, D.T. and C.P.; supervision, B.M.A.-H. All authors have read and agreed to the published version of the manuscript.  
This paper is part of the R&D project \"Cognitive Personal Assistance for Social Environments (ACOGES),\" reference PID2020-113096 RB-I00, funded by MCIN/AEI/10.13039/501100011033.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.