The algorithms have been applied to data collected in the Mathies
Mine after the robot returned. Table I summarizes
the results for the 3D scan matching and 6D SLAM. It is shown
that using approximate d-tree search decreases the running
time of the proposed scan matching algorithms about
15%. Nevertheless, the main speedup is reached by the data
reduction, resulting in a real-time capable ICP algorithm. The 6D
SLAM algorithm can be used on an inspection robot for mines, the
time needed for global consistent registration roughly
corresponds to the time, needed to drive to the next scanning
pose.
points used | time | # ICP iterations |
all points & brute force search | 4 h 25 min | 45 |
all points & ![]() |
6.8 sec | 45 |
all points & Apx-![]() |
5.9 sec | 45 |
reduced points & Apx-![]() |
![]() |
42 |
3D SLAM with | 42 (step 1) | |
reduced points & Apx-![]() |
52 sec | 497 (step 3) |
Fig. 6 shows the result of the Mathies Mine
mapping. The top plot shows the 2D map, i.e., -map, where
is the depth axis. The bottom part shows the elevation, i.e., the
-map. The Groundhog robot had to overbear a height of 4
meters during its 250 meter long autonomous drive.
To visualize the scanned 3D data, a viewer program based on OPENGL has been implemented. The task of this program is to
project the 3D scene to the image plane, i.e., the monitor, such that
the data can be drawn and inspected from every
perspective. Fig. 2 and 4 show
rendered 3D scans. A video of all matched 3D scans is available
for download at www.ais.fraunhofer.de/ARC/3D/mine/.
C
This paper has presented a new solution to the simultaneous
localization and mapping (SLAM) problem with six degrees of
freedom. Based on the ICP algorithm the registration error is
globally spread over all 3D scans and thus minimized. The
presented algorithms are significant speeded up with data
reduction that maintains the surface structure and with
approximate d-tree for closest point search.