# An Iterative Closest Point Method for Lidar Odometry with Fused Semantic Features
###### Abstract  
Lidar sensors play a pivotal role in a multitude of remote sensing domains, finding extensive applications in various sectors, including robotics, unmanned aerial vehicles (UAVs), autonomous driving, and 3D reconstruction, among others. Their significance and versatility in these areas have made them indispensable tools for a wide range of applications. The accuracy of Lidar odometry (LO), which serves as the front end of SLAM, is crucial. In this paper, we propose a novel iterative closest point (ICP) technique that combines semantic features to improve LO precision. First, the semantic segmentation neural network is used to extract the semantic features from each frame of the point cloud. Then, the obtained semantic features assist in extracting the local geometric features of the point cloud. Also, the residual blocks of the ICP algorithm's least squares are combined with semantic confidence functions to better predict the exact pose. Compared to LOAM, there is an average improvement of 4 cm in accuracy per 100 m. Experimental results illustrate the superiority of the proposed method and indicate that the fusion of semantic features can robustly improve the precision of LO. Lidar odometry; ICP; semantic segmentation +
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navigation, and drones. For example, Cartographer [7], developed by Google, is a notable system for robot navigation and map construction, showing significant progress in both 2D and 3D Lidar-based SLAM. LOAM, proposed by [PERSON] et al. [8] at CMU, is a 3D Lidar odometry and mapping algorithm known for its sparse mapping approach. It primarily matches feature edges and planes for mapping. The algorithm comprises two threads: the odometry thread, running at a high frequency, and the mapping thread, which matches the current point cloud with the map to provide lower-frequency precise pose estimates. This algorithm is straightforward, highly efficient, and, due to its scalability, serves as the foundation for various LOAM-based extensions [9; 10; 11]. As part of the LiDAR-based SLAM system, Lidar odometry (LO) focuses on the accuracy of the pose trajectory obtained through feature matching algorithms, which is also the primary focus of our work. The key to the LO algorithm lies in feature matching, where high-quality feature matching determines the precision of pose estimation. To ensure the stable identification of good features by the LO algorithm over an extended period, we utilize semantic information obtained from a semantic segmentation model to assist in feature extraction and feature matching within the LO system. To address the problems of traditional ICP on LO, we upgrade the inference model ([[https://github.com/vincenzo0603/rangenet_lib_forTensorRT8](https://github.com/vincenzo0603/rangenet_lib_forTensorRT8) XX]([https://github.com/vincenzo0603/rangenet_lib_forTensorRT8](https://github.com/vincenzo0603/rangenet_lib_forTensorRT8) XX), accessed on 9 September 2022) of the RangeNet++ framework to accelerate the acquisition of semantic labels. We propose a novel approach with a semantic confidence score, which is a LO with the fusion of semantic features and geometric features. The main contributions are summarized as follows:  
* A robust and accurate geometric feature selection strategy is proposed, which incorporates semantic information to select plane points and corner points based on curvature. * To improve the matching accuracy of points, the semantic confidence score in the residual block of least squares is adopted in the proposed semantic feature-based ICP algorithm. Section 2 introduces and discusses related work, including an overview of the core semantic segmentation algorithm and point cloud registration algorithm. Section 3 introduces the proposed LO algorithm framework. In Section 4, we elaborate on the methods and details of our framework implementation.