This thesis had three research questions to answer, as stated in Section 2.6, the questions were: 1. How do genuine real world sensors work with real world SLAM algorithms?
(a) How does nominally perfect odometry and odometry based off a real IMU influence the quality of map produced by sensor/SLAM algorithm combinations?
2. How do different type of sensor errors and limitations influence the mapping performance of SLAM algo- rithms in realistic simulated situations?
3. How effective are current map quality methods and can they be improved?
In response to these questions, the following are conclusions that were made based on the results from the experiments conducted in Chapter 4 and 5
• Research Question 1.
– Odometry error can influence the quality of map that is produced by various sensor and SLAM
algorithm combinations. The odometry error caused maps to be generated with the following map errors: ghost maps, elongation, and excess noise.
– With nominally perfect odometry, the optimum combination was found to be the Gmapping SLAM
algorithm and LightWare sensor. This combination generated maps with a low amount of error and variance, as well as a 100% map generation success rate.
– Where odometry was polluted with noise, the Gmapping and LightWare sensor was again found to
be the optimum combination. Again, this combination was chosen as the most optimum combination as it had the highest map generation success, as well as the lowest amount of map variance and map error.
– When the odometry was polluted with drift and noise, Hector Mapping and the Velodyne LIDAR
was found to be the optimum combination. Hector Mapping was the only SLAM algorithm to have significant map generation success. This is again due to the fact that wide FoR sensors could capture more features then a low FoV sensor. The more features there are present in a scan, the better scan matching algorithms can perform.
• Research Question 2.
– From the nominally perfect odometry results seen in Chapter 5, it was concluded that the low FoV
of certain sensors were in fact the cause of Hector Mapping failing to generate maps. This is further reinforced by the results obtained from the simulations. This failure to generate maps was due to the lack of features that was captured in each scan. The lack of features meant that the scan matching algorithm failed to match incoming scans and thus create a map
– In a nominally perfect odometry scenario, Gmapping and KartoSLAM had no issues with the limited
FoV that was present on certain sensors. In a simulated scenario, both Gmapping and KartoSLAM was able to generate the same result and thus show that the SLAM algorithms have a higher com- patibility with a range of sensors then Hector Mapping.
– When the odometry was polluted with noise, KartoSLAM and Gmapping had a much lower map
generation rate with the low FoV sensors. This was seen with the simulated and real sensor data. However, it wasn’t seen with most of the wide FoR sensors which suggests that the low FoV sensors did affect the SLAM algorithms capability to handle odometry polluted with noise.
– The effect of the sensor limitation on the SLAM algorithm was truly seen when Hector Mapping
failed to generate maps with the LightWare sensor when the odometry was polluted with noise. This failure was due to the low rotation rate of the LightWare sensor and was confirmed by the simulated results. In the simulations, the LightWare sensor was unable to create any map with Hector Mapping as the low rotation rate meant that scan data was incoming to the SLAM algorithm at a rate that was too slow to allow a map to be generated. This is a perfect example of how a wide FoR sensor can sometimes be an inefficient choice as sometimes the wide FoR sensors sacrifice rotation rates for the ability to see 360◦views.
– When the odometry was polluted with drift, the effect that the low FoV sensors had on the SLAM
algorithm was clear. None of the low FoV sensors was able to generate a map with any of the SLAM algorithms. Only the wide FoR sensors had success in generating a map.
• Research Question 3.
– The existing map quality method (the Santos Metric) proved to be unreliable as it frequently misin-
terpreted maps with the incorrect amount of error. That is, many of the maps were laced with map errors inside the area of interest but the Santos Metric still analysed the map with low amounts of error.
– The Reversed Santos Metric proved to be a more effective map quality metric as it was able to provide
results where maps were analysed with a more appropriate amount of error. That is, maps which contained map errors were analysed with a higher amount of error then maps which contained less map errors. While this was the overall goal, it was found that it was still affected by map errors that were outside the area of interest.
– The Minimum Map Metric was created to remove excess data that was outside the area of interest.
This meant the results would not be affected like the Reversed Santos Metric was. While it did improve the results, there was still issues with some of the features behind the Minimum Map Metric. One of the issues was that it was difficult to decide how to compensate for maps which had sections of the map missing. To overcome arbitrary issue of adding a penalty, it was decided that the results from the Minimum Map Metric would be shown in separated amounts. That way the reader can decide which parts of the map they would prefer to penalise more.