# POU-SLAM: Scan-to-Model Matching Based on 3D Voxels
## 1 Introduction  
Reliable localization of autonomous vehicles is an attractive research topic since it is a basic requirement for navigation and other tasks [1; 2; 3; 4; 5]. Fully autonomous vehicles should reliably locate themselves, ideally by using only their own sensors on board without relying on external information sources such as GPS. In scenarios where such external information is absolutely unavailable, Simultaneous Localization And Mapping (SLAM) is always an alternative option. SLAM has been intensively studied in the past few decades and various solutions based on different sensors have been proposed for both indoor and outdoor environments. In outdoor scenes, the advantage of LiDAR with respect to cameras is that the noise associated with each measurement is independent of the distance itself and usually more robust to the illumination variation. Therefore, laser-based SLAM is becoming one of the mainstream solutions in outdoor scenes. Most laser-basedSLAM methods tend to extract stable features such as planes and edges from points, and then do SLAM in a 'feature space'. They are therefore categorized as feature-based SLAM, whose performance is mainly determined by two factors. The first factor is the way features are designed. [PERSON] et al. [6; 7] propose a planar and edge feature designing method based on curvature. [PERSON] et al. [8] incorporate shape information which is calculated from geometric statistics of the point cloud into the Iterative Closest Point (ICP) correspondence step. The second factor is the scan matching method. Scan-to-scan and scan-to-model matching are the two main scan matching frameworks in SLAM. [PERSON] et al. [9] propose a scan-to-scan odometry trajectory estimation method by using convolutional neural networks to process LiDAR point clouds. KinectFusion [10] implements scan-to-model matching by using an implicit surface as model. The state-of-the-art laser-based SLAM method, LiDAR Odometry And Mapping (LOAM) [6] extracts distinct features corresponding to physical surfaces and corners. To enable on-line implementation, LOAM switches between scan-to-scan at 10 Hz and scan-to-model operation at 1 Hz update frequency. Although LOAM has achieved good performance, there are still challenges in outdoor applications using 3D laser sensors, i.e., (1) inherent point matching error since the sparsity of the point clouds, which means that there are always existing errors when we directly use the sparse point cloud to fit the surface of environment. Furthermore, it is nearly impossible to obtain rigid point correspondences between scans; (2) it is hard to fix the matching parameter since the density variation of the point clouds. During the feature-based matching process, it is required to extract enough context information of one 3D point to make it discriminative, while the scope of the dominating context is greatly affected by the point density. In this paper, to tackle the above challenges, we introduce POU implicit surface representation to regress the environment surface with sparsity points, which can effectively encode the detailed geometrical information. We discretize the point cloud into 3D voxels. These voxels are classified into planar and edge feature voxels according to the geometrical character of the points contained in the voxel. Finally, we build a feature voxel map for further scan-to-model matching, during which the POU implicit surface representation is proposed to adapt to the voxel map. The key contributions of our work are as follows:  
1. We propose a novel feature voxel map which stores voxels with salient planar or edge features. 2. We propose a scan-to-model matching framework using POU implicit surface representation. The rest of this paper is organized as follows. In Section 2, we discuss related work and conclude how our work is different from the state of the art. The feature extraction Algorithm 1 is presented with details in Section 3, and the scan-to-model matching Algorithm 1 is given in Section 4. Experimental results are shown in Section 5.