# Algorithm for Determining the Position of a Ship Hull-Cleaning Robot
## 5 Data Sets
### Rosario Dataset  
Before developing the project-related data and models, we decided to test the above-mentioned methods on some benchmark dataset, and the Rosario dataset [12] became our choice.  
The Rosario dataset was collected using a mobile robot (Figure 2) designed for outdoor agricultural environments [12]. The robot was equipped with a TARA stereo-inertial sensor (LSM6 DS0 IMU), operating at a frequency of 140 Hz. For global localization, the system includes a GPS-RTK Emlid Reach module, operating at a frequency of 5 Hz. A ZED stereo camera was also mounted on the robot to collect synchronized left and right images at a resolution of 672 \(\times\) 376 px and at a frame rate of 15 Hz.  
Data were recorded on the open grounds of the Universidad Nacional de Rosario (Argentina), specifically in a grassy field used for testing mobile robotics in agricultural-like  
Figure 2: The Rosario robot and its sensors [12].  
outdoor environments. The ROSARIO dataset consists of six recorded routes of varying difficulty collected by the robot in soybean fields. The dataset contains synchronized readings from the following sensors (the measurement frequency is given in brackets):  
* odometry (10 Hz);
* IMU (140 Hz);
* GPS (5 Hz);
* stereo cameras (this information was not used in the experiments). In particular, the information provided by each sensor is as follows:
* time stamp,
- calculated position of the robot in the XYZ coordinate system,
- linear velocity,
- angular velocity.
* time stamp,
- orientation of the robot in free space in the form of quaternions,
- angular velocity,
- linear acceleration.
* time stamp,
- linear velocity,
- angular velocity
- position covariance defined with respect to the tangent plane.  
In this work, we assume the GPS-RTK position as ground truth, as the system used (GPS-RTK Emlid Reach) has been shown to achieve centimeter-level accuracy. The precision of this GPS-RTK configuration has been evaluated in [40], where the mean positioning error in Fix mode (the state in RTK-GPS positioning where the receiver has successfully resolved carrier phase ambiguities) was reported to be around 5 mm with a sampling rate of 5 Hz.  
As mentioned, the data include six runs with a total length of 2296.93 m, completed in 34.7 min. The first run was in the north-south direction, while the rest, according to the suggested robot movement pattern, was in the east-west direction. The summary of the length and duration of each run is presented in Table 1. The actual course of each run is shown in Figures 3 and 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sequence** & **Length [m]** & **Duration [s]** \\ \hline
1 & 615.15 & 558 \\
2 & 320.16 & 264 \\
3 & 169.45 & 198 \\
4 & 152.32 & 162 \\
5 & 330.43 & 312 \\
6 & 709.42 & 588 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the distance and duration of each of the analyzed sequences.  
The motivation for using position correction models (in relation to the values resulting from odometry alone) was the observation that, based only on odometry, we obtained significant differences between the actual (GPS) and estimated (odometry) positions. The size of these differences is shown in Figures 5 and 6--they correspond to the first and fifth courses from the Rosario data; the dashed line is the actual course according to GPS, and the  
Figure 4: Visualization of the remaining sequences.  
Figure 3: Visualization of the first sequence.  
continuous line is the course resulting from the odometry data. The color of the continuous line reflects the difference in positions (from blue as small error to red as large error).