# An Iterative Closest Point Method for Lidar Odometry with Fused Semantic Features
## 6 Discussion  
In terms of robustness, our proposed method exhibits superior performance in translational error accuracy, maintaining stability across various scenarios. From a scene perspective, the SuMa framework performs best in 6 out of 11 scenarios. However, SuMa's performance varies significantly across different scenes, particularly with the highest error observed in sequence 01 (highway), suggesting its suitability for non-highway scenarios. Although our proposed method is generally more robust, the improvement in pose accuracy is not substantial across most scene environments. Analyzing the scene types, all methods struggle in sequence 01 (highway) scenarios. Even the proposed method, which performs better than other approaches, exhibits a relatively high average translational error of up to 1.27 m. Several factors contribute to these challenges. First, highways typically lack many structures on both sides, reducing the extraction of feature points and impacting matching accuracy. Second, the higher vehicle speeds on highways result in greater point cloud distortion compared to slower-moving vehicles. Third, the presence of fast-moving dynamic objects on highways, such as other vehicles, contradicts the assumption of a static environment made by the SLAM models. If some of the extracted feature points are associated with dynamic objects, this can affect pose accuracy. In summary, highway scenes pose significant challenges due to their sparse geometry, fast-moving dynamics, and the presence of many dynamic objects. These factors contribute to the substantial errors observed in different algorithm frameworks in highway scenes. As indicated by our analysis, highways present a challenging environment, and our proposed method helps mitigate pose errors to a certain extent. Lidar can also incorporate data from inertial measurement unit (IMU) sensors. IMUs are sensors used to measure an object's linear acceleration and angular velocity, typically comprising accelerometers and gyroscopes. By leveraging the real-time data from IMUs alongside the precision of Lidar distance measurements, the fusion of these two sensor types significantly enhances the accuracy of pose estimation, especially in dynamic or complex environments. In addition to IMUs, the Global Positioning System (GPS) is another common supplementary sensor, particularly in outdoor scenarios. GPS provides global positioning information, aiding in global localization and map calibration. In some situations where SLAM systems might lose sensor data (e.g., Lidar or camera failure), GPS signals remain accessible. In such cases, GPS can offer an emergency means of localization to maintain the positioning of robots or devices. In the future, we are interested in researching semantic-assisted multi-sensor fusion odometry, such as semantic visual-Lidar odometry (SVLO), where semantic features are fused. Following the camera-Lidar joint calibration, a relationship between pixel points and point clouds can be established through projection parameters (intrinsic and extrinsic), allowing simultaneous acquisition of semantic labels for both pixel points and point clouds through image semantic segmentation. Establishing a semantic-assisted visual odometry serves as the prior pose for the proposed semantic fusion Lidar odometry in this paper. Additionally, exploration will be undertaken to integrate IMU sensors. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Point Cloud**} & \multicolumn{2}{c}{**Geometric Feature Extraction**} & \multicolumn{2}{c}{**Semantic-Aided Extraction**} \\ \cline{2-5}  & **Plane Feature** & **Edge Feature** & **Plane Feature** & **Edge Feature** \\ \hline
122,471 & 4226 & 832 & 1635 & 436 \\
107,637 & 4135 & 765 & 1434 & 433 \\
113,722 & 4374 & 803 & 1335 & 399 \\
122,336 & 5047 & 804 & 1168 & 268 \\
121,301 & 4737 & 789 & 1373 & 439 \\
117,365 & 4856 & 857 & 1469 & 393 \\ \hline \hline \end{tabular}
\end{table}