# Study of the Effect of Exploiting 3D Semantic Segmentation in LiDAR Odometry
## 2 Related Work  
LiDAR odometry aims to estimate the position and orientation of a movable platform by tracking the 3D points obtained by an onboard scanner. Usually, the odometry procedure is carried out by means of an incremental approach that computes the relative transform between the sensor poses at two different time steps; then, the absolute pose can be retrieved as the accumulation of all these transforms [4].  
This paradigm necessarily requires matching consecutive point clouds. 3D data from modern multi-layer devices offer a multitude of features that can be used to establish correspondences. In turn, this also involves increased computational requirements to perform the matching. Some approaches have been proposed to mitigate the data processing burden with strategies that include projecting the LiDAR scan to a ground plane [5] or using a siamese network able to learn dimension-reduced representations [6].  
Figure 1: Samples of point clouds resulting from some of the tested configurations. Letters (**A**,**B**,**E**,**F**) refer to different configurations, described in Section 3.2.  
The incremental nature of odometry implies that the method is prone to drift, caused by the accumulation of errors during the complete trajectory. For this reason, odometry is often included within a complete SLAM procedure able to estimate both the location and the map of the environment jointly. In that way, the algorithm is able to detect when the current location corresponds to a previously visited area, reducing the drift in the trajectory. One of the most popular LiDAR-based SLAM approaches nowadays is LOAM [3], which splits the SLAM problem into two concurrent procedures: high-frequency odometry and low-frequency mapping. LOAM, which makes use of edge and planar features selected according to a roughness estimate, has proven extremely effective in terms of localization, as shown by some public benchmarks such as KITTI [7]. More recently, IMLS-SLAM [8] reached similar levels of accuracy by using the Implicit Moving Least Squares surface representation.  
The inclusion of additional data, such as images [9] or IMU [10] measures, has been often exploited to increase the performance of LiDAR SLAM. However, one of the most promising lines of research in this topic is the thoughtful preprocessing of data fed to the algorithms. For instance, LeGO-LOAM [11] is a lightweight version of LOAM that filters out noise and takes advantage of the ground plane. Another group of methods focuses on leveraging semantic information about the 3D points, such as SuMa++ [12], which embeds these labels into a surfel-based map representation, or SLOAM [13], oriented towards forest environments.  
With the advent of deep learning, an increasing number of approaches have made use of convolutional neural networks to obtain features using learned extractors [14]. Recurrent neural networks, such as Long Short-Term Memory (LSTM), have also been employed to model connections among consecutive scans [15]. Despite this, the potential of deep learning approaches to significantly outperform geometrical alternatives is still to be proven.