## 5 Discussion
### Comparison with Other Positioning Technologies  
To better understand the strengths and limitations of the proposed ultrasound-based localization system, it is compared with other common positioning technologies: vision-based systems, LiDAR, SLAM, and traditional odometry. Table 1 provides a comprehensive comparison of these technologies across key performance metrics, including localization accuracy, scale, system cost, sensor cost, and adaptability. Vision-based localization systems have gained popularity due to their ability to provide rich environmental data. However, these systems often struggle in low-light conditions or environments with limited visual features. In contrast, the ultrasound system operates independently of the lighting conditions, making it more versatile in various indoor settings. Furthermore, the ultrasound system generally requires less computational power compared to complex image processing algorithms, offering a more cost-effective solution. This lower processing requirement is a significant advantage for applications where power consumption is a concern. LiDAR (Light Detection and Ranging) is known for its high accuracy, fast update rates, and excellent range and resolution, which makes it well suited for outdoor or large-scale indoor applications. Although LiDAR provides superior performance in these aspects, the ultrasound system is generally more cost-effective, particularly for smaller-scale applications, and offers absolute positioning in real time without the need for additional algorithms. The ultrasound system typically consumes less power than LiDAR. However, LiDAR's superior range and resolution make it more suitable for applications requiring \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Technology** & **Accuracy** & **Scale** & **System Cost** & **Sensor Cost** & **Adaptability** \\ \hline US Beacon & Medium & Room-level & Low & Low & High \\ Vision SLAM & Medium & Room-level & Medium & Medium & Low-Medium \\ LiDAR SLAM & High & Medium-scale & High & High & Low–Medium \\ Odometry & Low & Medium-scale & Low & Low & High \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of localization technologies. detailed environmental mapping or long-range sensing. Simultaneous Localization and Mapping (SLAM) has become increasingly popular, especially with the availability of cost-effective sensors. While SLAM offers flexibility in unknown environments, it may be less reliable in dynamic settings compared to the proposed ultrasound system. The beacon-based system generally has lower computational needs compared to most SLAM implementations, allowing for simpler, more power-efficient hardware. Furthermore, the proposed system provides absolute positioning within the beacon network, while SLAM typically provides relative positioning that may drift over time without loop closure. The ultrasound-based approach can be more robust in dynamic environments where the physical space is changing, which can challenge SLAM algorithms. Traditional odometry, often using wheel encoders or Inertial Measurement Units (IMUs), offers a cost-competitive alternative for robot localization. Like the ultrasound beacon-based system, odometry solutions can be implemented with relatively inexpensive hardware and have minimal computational requirements. However, odometry suffers from cumulative errors over time, leading to a drift in position estimates. This is particularly problematic for long-term operation or after sharp turns. In contrast, the ultrasound-based system provides absolute positioning that does not degrade with time. While odometry can offer very high update rates, making it suitable for real-time control of fast-moving robots, it often needs to be combined with other localization methods for reliable long-term operation. Recent advances in SLAM and other advanced positioning strategies have made significant progress, particularly with the increasing availability of cost-effective LiDAR and camera solutions.