## 2 Related Works
Although this type of computation is correct when offloaded to GPU, it should be noted that not all calculations are suitable and some must be offloaded to CPU when more serial processing power is needed. It also requires additional hardware which increases the cost of a GPU. Some other techniques than GPU processing are done using a simplified scan or map dataset. Saving full map data in every scan sometimes is not efficient for a scan matcher. The higher map detail or resolution results in a higher dimension of total number of data to hold the map. Thus, it consumes more massive computational resources as compared with smaller maps. In [12; 13; 14; 15], they introduced a simplified map by using a grid-based map. For every scan of the sensor, first, the data are down sampled in order to reduce map information. The scan is either down sampled to a two-dimensional (2D) grid space or to a 3D space using normal distribution transform (NDT) [16; 17] clustering if the sensors are using 3D-based LiDAR. As the map becomes less complicated, the scan matcher processes the data faster, however, the accuracy of the low-resolution map largely depends on a map feature that stands out such as a structure, pole, etc., and the grid size of the map dictates the minimum distance error variance of the pose. In addition to grid-based maps, there is also a feature- or topography-based map which saves some important features from the sensor scan rather than full-scan data such as planar surfaces, edge lines, landmark, etc. [18; 19; 20; 21]. SLAM that uses a topography map requires feature pattern training to recognize the object from the scan and use it as a landmark on the map. While the vehicle is moving, SLAM records the position of landmarks or features and saves them inside the topography-based map. The pose is then calculated by associating the recognized object in each sensor scan against the related saved feature objects in the topography map. The pose generation is faster than that of a method utilizing a normal map as it skips plenty of information outside the referred features. However, if the environment lacks the data of the referred features, it can result in poor pose calculations. There are other techniques to improve the performance of computational speed such as a multi-resolution map for branch and bound scan matching implemented in Google's Cartographer technique [1, 22]. It is a scan matcher applied on a multi-resolution grid map. In Cartographer, map grids with lower resolution are utilized for the initial pose estimation. Later on, the initial pose accuracy is further increased using a scan matcher on higher resolution. Of course, by using known initial pose estimation, it can skip various pose iterations of the scan matcher on a higher resolution map. Thus, the computational speed is improved without sacrificing the accuracy of the map and pose generated. In this paper, we propose another optimization method inspired by the multi-grid resolution map from Google's Cartographer technique. Instead of using multi-resolution maps, the proposed method uses a scheduler to filter distance size of the LiDAR sensor. It is known in SLAM that sensor distance can affect map-sampling size. A bigger map sampled helps reduce the drift error since more feature data are added, however it is not necessary to use full sensor data every time to create a bigger map. The scheduler works to bridge the sensor distance, which swaps between a small and full-scan size, and therefore the pose estimation using a scan matcher can be faster.