## 4 Experimental Results
### KITTI Benchmark  
In the first validation test, the KITTI dataset used hardware that consisted of Velodyne 64 LiDAR sensors coupled with the IMU. These sensors were mounted in a station wagon which collects data in urban street areas around Karlsruhe, Germany. In our case, the computing specification to test the SLAM used the AMD quad core 3.2 Ghz CPU installed with a robot operating system (ROS) [28]. The data of the LiDAR from the KITTI were also filtered to use a maximum limit of 60 m scan distances from the original 100 m. This reduced sensor noise and distortion found in the rotating LiDAR during high speed motion [29]. The AMDS was set to use long distance scans of 60 m and short distance scans of 25 m, respectively. Then, the methods were compared to the Vanilla Cartographer technique using the two scan states. The grid size of the map in all cartographer configurations was set to use 0.2 m in resolution. The accuracy performance analysis of the AMDS was analyzed by comparing the local SLAM performance of AMDS to the Cartographers' performance with fixed distances; as well as the AMDS after the global SLAM optimization. A good local SLAM performance resulted in fewer drift errors which guaranteed that global SLAM more easily recognized the previously visited area. When the drift was too large Cartographer failed to perform optimization. Thus, global SLAM optimization in Cartographer requires different tuning parameters to adapt map conditions and sensor configuration (e.g., scan matcher score threshold, search window size, scan matching iteration, etc.) unlike in local SLAM. For the first experiment, the result using the KITTI dataset is shown in Figure 5.  
Figure 5: Trajectory results of the KITTI benchmark: (**a**) Map location and (**b**) trajectory results. In the KITTI benchmark, the AMDS (60 m/25 m) local SLAM achieves similar accuracy as compared with Cartographer (60 m scan) local SLAM. The Cartographer (60 m scan) local SLAM achieves maximum error 0.9 m differences against the ground truth while the AMDS (60 m/25 m) have around 1 m, however, the Cartographer (25 m scan) technique produces a higher error around 4.9 m from ground truth. From the analysis, higher errors from Cartographer (25 m scan) are due to the lack of environmental structure sampled. The area used in the KITTI dataset contains rich structure environment, and thus a scan matcher in the Cartographer technique easily matches straight line shapes as the key features in the map. Other features such as vegetation, poles, and tree trunks are also utilized as the Cartographer scan matcher's key frame, but, most of the time, structures such as walls are easily identified by the scan matcher to connect building lines. The results of the scan matcher in smaller scans, however, do not have enough information to match the lines of structures against building lines at far distances. As a result, the scan matcher cannot provide accurate judgments when estimating motion continuously. Unlike the Cartographer (25 m scan) technique, the AMDS (60 m/25 m) suffers slightly from this because the AMDS is still utilizing a large scan to provide the scan matcher with more accurate motion judgments. Meanwhile, the global optimization of the Cartographer technique, when using the AMDS, successfully reduces the impact of drift error from 1 m error from ground truth to 0.5 m. For the mean error against ground truth, AMDS (60 m/25 m) has an average of 0.36 m, whereas AMDS with global optimization reduces the mean error to 0.2 m. The result of error against ground truth can also be seen from the map generated, as shown in Figure 6.  
Figure 6: KITTI map results: (**a**) 25 m scan, (**b**) 60 m scan, (**c**) AMDS (60 m/25 m), (**1**) full map, and (**2**) zoomed starting point. In Figure 6, the data obtained from the 25 m scan gave higher errors in positions from the known ground truth. This is seen from the starting point area where the building structures are not properly aligned.