# IMU-Aided High-Frequency Lidar Odometry for Autonomous Driving
## 3 The Proposed Approach
### Output Step: Fusion in an EKF Framework
#### 3.6.3 Measurement Model
\begin{table}
\begin{tabular}{c c} \hline
**Speed (km/h)** & **Methods of Assigning Initial Guess** & **Mean Relative Position Error** \\ \hline
25 & IMU & 0.31\% \\  & Previous regist & 0.46\% \\ \hline
60 & IMU & 0.57\% \\  & Previous regist & 1.32\% \\ \hline \end{tabular}
\end{table} 
Table 3: Error comparison of different methods at the intermediate step. #### 4.3.3 Tests in Both Urban and Off-Road Environments

Our algorithm is tested in both urban and off-road environment. We choose to use LOAM as the lidar odometry algorithm. The point clouds are rectified by the IMU data. In the urban environment, the vehicle drives at a speed of around 25 km/h, and the overall distance is about 1.1 km. In the off-road environment, the vehicle drives at a speed of around 15 km/h and the overall distance is about 1.0 km. The trajectories generated by our method are compared with the ground truth in Figure 12. A detailed error comparison is shown in Table 5. It is seen that our approach is applicable for both the urban and off-road environments, whilst the error in the off-road scenario is slightly higher than the urban environment. ### Experiments in GNSS-Denied Environment

Finally, we test our whole localization system in the GNSS-Denied environment. Figure 13a shows the trajectory generated by our localization system when the UGV drives at a speed of 20 km/h. By projecting the trajectory to the satellite image, we find that the position drift is quite small. Figure 13b compares the trajectories generated by our localization system and by the disturbed GNSS/INS system. Figure 13c shows the output frequency of our localization system. The average output frequency is around 39.7 Hz. In summary, no matter whether the GNSS signal is available or not, our system can output precise and high-frequency localization results. Figure 12: The results in the urban environment and off-road environment. **(a)** shows the trajectory generated in the urban environment when the UGV drives at a speed of around 25 km/h, and **(b)** shows the trajectory generated in the off-road environment when the UGV drives at a speed of around 15 km/h. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Environment** & **Distance (km)** & **Mean Relative Position Error** \\ \hline Urban & 1.097 & 0.31\% \\ Off-road & 0.983 & 0.93\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Error comparison of diverse environments. ## 5 Conclusions

In this paper, we present a novel approach for combining the IMU data with lidar odometry. The proposed localization system could generate stable, accurate, and high-frequency localization results. Since our system does not recognize the loop closure, we can further extend our work to enable the ability of loop-closure detection. Conceptualization, H.F.; Data curation, H.X. ; Formal analysis, H.X. ; Funding acquisition, B.D. ; Investigation, H.X. ; Methodology, H.F. and H.X. ; Project administration, B.D. ; Resources, H.F. and B.D. ; Software, H.F. and H.X. ; Supervision, B.D. ; Validation, H.F.; Original draft, H.X. ; Review and editing, H.F. This work was supported by the National Natural Science Foundation of China under No. 61790565. The authors declare no conflict of interest. ## References

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Figure 13: The results in a GNSS-deniedenvironment.