# POU-SLAM: Scan-to-Model Matching Based on 3D Voxels
## 2 Related Work  
SLAM with cameras and LiDAR has attracted wide attention both from robotic and computer vision communities. We acknowledge the large body of work in this field, but concentrate here only on approaches based on LiDAR. Since feature extraction, laser-based mapping and scan-matching framework are the three most important steps for laser-based SLAM, we therefore look into related work on these three aspects and provide general ideas on how they work as following.  
**Feature extraction:** LOAM focuses on edge and planar features in the LiDAR sweep and keep them in a map for edge-line and plane-planar surface matching. [PERSON] et al. [11] also use edges and planar features to solve different components of the 6-DOF transformation across consecutive scans. While these works extract features based on curvature, there is still difference, as the method by [11], which is ground-optimized, as it leverages the presence of a ground plane in its segmentation and optimization steps. Using point cloud obtained from a high resolution and high density Zoller-Frohlich Z+F laser, [PERSON] and [PERSON] et al. [12; 13] exploit point cloud geometrical statistics to classify the natural terrain into scatter-ness, linearity, and surface-ness. Using point cloud from a SICK LMS291 laser range sensor, [PERSON] et al. [8] implement scan-matching based on point cloud geometrical statistics features. They use ICP to align voxel centroids, which can reduce the number of points for scan matching as well as the size of the map. In contrast to [8], we consider the geometric features of the points more subtly and implement scan matching with original points, which would not lead to information loss.  
**Laser-based mapping:** SLAM systems often build and maintain environment maps, such as the feature map [6; 7; 11; 14; 15], dense map [16; 17], subsampled clouds [18; 19] and Normal Distributions Transform (NDT)-based map [20; 21]. In laser-based SLAM systems, the feature map is usually a structural collection of surfaces and corners that are extracted from original point clouds. In this paper, we build a feature map, which can be distinguished from methods in the literature in the way that we maintain more types of features in the form of voxels.  
**Scan matching framework:** Recently, some laser-based SLAM systems use scan-to-model matching framework and achieved state-of-the-art performance using the KITTI odometry benchmark. [PERSON] et al. [16] propose a dense approach to laser-based SLAM that operates on point clouds obtained from Velodyne HDL-64. They construct a surfel-based map and use a point-to-plane ICP [22; 23] to estimate the pose transformation of the vehicle. Our method essentially belongs to scan-to-model matching framework. The distinction is that we build a feature voxel map and use POU implicit surface representation to adapt to the scan-to-model matching process based on feature voxel map.  
**Partition of unity approach:** The POU approach has been widely used in surface reconstruction. [PERSON] et al. [24] show how to reconstruct multi-scale implicit surfaces with attributes, given discrete point sets with attributes. [PERSON] et al. [25] propose a new 3D non-rigid registration algorithm to register two multi-level partition of unity implicit surfaces with a variational formulation.