Vehicle odometer plays an important role in unmanned driving systems, and its main task is to determine the position and pose of the vehicle. The calculation method falls into two categories of visual odometry (VO) and LiDAR odometry (LO). VO is limited by illumination conditions, while LO is unaffected by illumination changes through active laser beam emission, making LO more suitable for vehicle odometry measurement than VO. However, the traditional LO algorithms face the problems of low accuracy and weak robustness when they are employed to register the continuous LiDAR frame point clouds without initial value and long sequence. We introduce an end-to-end point cloud registration network HRegNet and propose an LO computation algorithm using deep neural networks HRegNet-LO. Such a LO computation strategy exhibits strong robustness, good accuracy, and high efficiency, and we hope that the proposed algorithm can be helpful to the LO measurement.

The HRegNet-LO algorithm includes two core modules of front-end calculation and back-end optimization. The calculation is shown in Fig. 1.

In frond-end calculation, scan-to-scan registration is conducted to obtain the transformation of adjacent two LiDAR frame point clouds. Firstly, HRegNet is employed to calculate a rough transformation matrix between two adjacent LiDAR frames without initial value by adopting the original point clouds. Secondly, according to point curvature, some feature points belonging to different categories are extracted from the original point clouds. Thirdly, the rough transformation matrix is refined by an iterative closest point (ICP) algorithm using feature points. Fourthly, LO initial pose is obtained by the sequence registration of the transformation matrix between two adjacent LiDAR frames.

In back-end optimization, scan-to-map registration is conducted to adjust the LO initial pose. Firstly, a feature map is constructed using the initial LO pose and feature points. Secondly, by finding the corresponding feature points from the current LiDAR frame and feature map, a pose correctional value is obtained via the ICP algorithm. Thirdly, the LO pose of the current LiDAR frame is adjusted to reduce the estimated trajectory drift.

Comprehensive experiments are carried out on the Kitti odometry dataset to evaluate and demonstrate the performance of the proposed HRegNet-LO algorithm. The LOAM, F-LOAM, and some other methods are compared with our method.

Figures 5 and 6 show the qualitative results of HRegNet-LO on three experimental datasets. Figure 5 indicates that the measurement results of the HRegNet-LO algorithm along the x and z axes are in good agreement with the real pose information of the ground truth, while the measurement accuracy along the y axis is poor. This is because the point clouds obtained by the vehicle-mounted LiDAR system have more constraints in the horizontal direction (along the x and z axes), but fewer constraints in the vertical direction (along the y axis). Therefore, the positioning accuracy of the horizontal direction is better than that of the vertical direction. In practical applications, the horizontal component of LO measurement results is more important than the vertical component. To observe the horizontal measurement results of the proposed algorithm more clearly, Fig. 6 shows the aerial view of our algorithm's measurement results on the three testing datasets, where we can find that the horizontal component of the measured results is very close to the real path of the ground-truth. To conclude, the proposed algorithm can accurately realize the LO measurement only by LiDAR point clouds.

Quantitative and comparison experiments are also conducted, whose results are shown in Fig. 7 and Table 2. In terms of relative rotation error (RRE) and relative translation error (RTE), our method almost obtains the best LO measurement results, and the RRE and RTE per 100 m can be controlled at about 0.3° and 1 m respectively, which can meet the positioning accuracy requirements in unmanned driving.

Ablation experimental results are shown in Fig. 8 and Table 3, which show that the back-end optimization can improve the accuracy by about 70%.

We design a novel LO measurement method HRegNet-LO algorithm. The front-end calculation is realized by HRegNet deep neural network, and back-end optimization is realized by point clouds feature map. Experimental results show that the RRE and RTE of HRegNet-LO are about 0.003°/m and 1% respectively, and the consumed time of calculating each frame pose is about 100 ms. The proposed method can satisfy the accuracy and real-time requirements in LO measurement. However, we only consider using 3D coordinates of LiDAR point clouds to achieve LO measurement. Multi-system fusion methods such as integrated inertial navigation, vision, and LiDAR odometer measurement technology can be considered in subsequent studies to improve the reliability and accuracy of LO measurement results. Meanwhile, the generalization of deep neural networks also should be improved.