# Vehicle Pose Estimation Method Based on Maximum Correntropy Square Root Unscented Kalman Filter
## 5 Results
### Experimental Results and Analysis  
To further validate the performance of the proposed SLAM algorithm, we evaluated EKF-SLAM, UKF-SLAM, SRUKF-SLAM, MCUKF-SLAM, and our MCSRUKF-SLAM using a laser SLAM dataset collected in an outdoor parking environment by the Australian Centre for Robotics at the University of Sydney [52].  
This parking lot represents a complex semi-structured outdoor environment prone to generating non-Gaussian noise and significant outliers due to environmental variations and electromagnetic interference. Such realistic conditions provide an ideal testbed for validating both the robustness and precision performance of SLAM algorithms. The geometric parameters and sensor configuration of the data collection vehicle are shown in Figure 13. As illustrated, the experimental platform is a four-wheeled intelligent vehicle equipped with wheel encoders (ROD-430), a LiDAR sensor (SICK LMS 221), and a GPS receiver (Ashtech GG24). The wheel encoder was employed to measure the vehicle speed and steering angle, with standard deviations of 2 m/s and \(\pi\)/30 rad, respectively. A lidar system with a 180\({}^{\circ}\) frontal field of view (FOV) was utilized to estimate the range and heading angle of environmental features, exhibiting standard deviations of 1 m and \(\pi\)/60 rad, respectively. GPS was used to obtain the ground truth of the vehicle state for validating the performance of the SLAM algorithm. In this experiment, the steel pole wrapped with reflective material served as an artificial landmark to enhance the detection of feature-based fiducials by the lidar system.  
EKF, UKF, SRUKF, and MCUKF are widely adopted algorithms in SLAM systems. Therefore, in our experiments, the proposed MCSRUKF-SLAM algorithm was comprehensively compared against these baseline methods. The experimental results in Figure 14  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & **Total Running Time (s)** & **Single Step Time (s)** \\ \hline EKF-SLAM [13] & 232.265 & 0.206 \\ UKF-SLAM [20] & 250.393 & 0.222 \\ SRUKF-SLAM [23] & 240.196 & 0.215 \\ MCUKF-SLAM [39] & 329.703 & 0.293 \\ The proposed MCSRUKF-SLAM & **324.038** & **0.285** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Running time comparison of five algorithms under colored noise conditions.  
Figure 13: Basic parameters of the mobile vehicle for the real-world dataset experiments in car park: (**a**) vehicle model and parameters; (**b**) mobile vehicle.  
were obtained by averaging 30 Monte Carlo runs for each SLAM algorithm. In the figure, crosses (+) and squares (\(\square\)) denote the estimated landmark positions and ground truth landmark positions, respectively, while the dashed lines in different colors and solid black lines represent the robot's estimated trajectory and ground truth trajectory, respectively. Table 8 shows the ARMSE of different algorithms in the car park dataset. As shown in Figure 14 and Table 8, the estimation performance of EKF-SLAM is inferior to that of UKF-SLAM and SRUKF-SLAM, with UKF-SLAM and SRUKF-SLAM exhibiting comparable performance. MCUKF-SLAM outperformed SRUKF-SLAM, while the proposed MCSRUKF-SLAM demonstrates significantly higher accuracy than all other SLAM algorithms. Therefore, the experimental results in Figure 14 and Table 8 indicate that the proposed MCSRUKF-SLAM algorithm achieved remarkable localization precision in the real-world parking lot datasets.