## 4 Experimental Results
### Experimental Validation  
The second experiment tested the platform in exclusive road with various conditions. The hardware test-bed setup used Ouster OS1 LiDAR 64 layer together with an inertial measurement unit that modelled Spatial IMU by Advance Navigation. The field test was conducted in an actual environment using an MPV equipped with a LiDAR system. The computing specification used an AMD quad core 3.2 GHz processor. Figure 7 shows the experimental test-bed setup. In the second experiment, since the hardware used a custom setup, the sensor data could be easily adjusted to connect to other SLAM methods for comparisons, for example, NDT-SLAM and LOAM. The experiment was conducted with a similar preset configuration. The performance evaluation, however, did not use the known ground truth because RTK-GPS is very costly. In contrast to the KITTI vision benchmark suite, other researchers have found alternative methods to evaluate SLAM performance. [PERSON] et al. [30] measured distance differences during the map alignment in the correction and optimization processes of SLAM. The distance difference values in every optimization process were then averaged to find the relationship with SLAM consistency. Good SLAM performances were indicated when the correction and optimization processes do not have high fluctuation or distance differences. However, our work used a simpler approach because the SLAM which does not have optimization, such as LOAM, could not be assessed in such way. The SLAM's accuracy was validated through a closed loop run, where the vehicle must travel the road with the start and finish pose located at the same coordinate. Ideal trajectories generation is where the SLAM can provide identical finish pose at the exact original starting point. Thus, when the trajectories deviated from the actual  
Figure 7: An MPV sized vehicle for SLAM configuration. position (drift error), they are measured by measuring the distance differences between these two points. The details of the test conducted are shown in Figure 8.  
Test locations for the second experiment were conducted in the urban area of Cyberjaya, Malaysia, as shown in Figure 9.  
The test was done at a speed limit of 30 km/h to reduce rotating LiDAR sensor distortion at high speed [29]. According to Figure 9, the test locations for the environments of map 1 and map 2 are  
Figure 8: Measuring drift error in trajectories. Figure 9: Ariel views of test locations: (**a**) Map 1, (**b**) map 2, (**c**) map 3, (**d**) map 4, (**e**) map 5, and (**f**) map 6. On the basis of the trajectories generated, as shown in Figure 10, most of the results show the same patterns. The Cartographer local SLAM technique implementation using AMDS (60 m/25 m) results in minimal impact in trajectories as compared with the standard Cartographer (60 m scan) technique. The longest distances traveled in map 6 (2 km distances) result in 2 m distance differences versus Cartographer (60 m scan). The AMDS trajectories after optimization also give less drift error as compared with the heavy computational load with optimization NDT-SLAM, since NDT-SLAM does not perform online mapping. Meanwhile, the LOAM performance falls off in long distance travel due to the unavailability of optimization methods unlike the NDT-SLAM or Cartographer. LOAM also gives poorer results when there is no solid structure on the road nearby, as shown in map 6. The detailed table comparison of drift error can be seen in Table 4.