Where am I? Sensors and Methods for Mobile Robot Positioning

Chapter 8: Map-Based Positioning 189
Figure 8.2: Schiele and Crowley's robot models its position in a hallway.
a. Raw ultrasonic range data projected onto external coordinates around the robot.
b. Local grid and the edge segments extracted from this grid.
c. The robot with its uncertainty in estimated position within the global grid.
d. The local grid imposed on the global grid at the position and orientation of best
correspondence.
(Reproduced and adapted from [Schiele and Crowley, 1994].)
successor Mobot IV. (Note that the photograph in Figure 8.3 is very recent; it shows Mobot IV on
the left, and Mobot V, which was built in 1995, on the right. Also note that an ORS-1 lidar from ESP,
discussed in Sec. 4.2.2, is mounted on Mobot V.)
A typical scan from the in-house lidar is shown in Figure 8.4. The similarity between the scan
quality of the University of Kaiserslautern lidar and that of the ORS-1 lidar (see Fig. 4.32a in
Sec. 4.2.6) is striking.
The angle histogram method works as follows. First, a 360 degree scan of the room is taken with
the lidar, and the resulting “hits” are recorded in a map. Then the algorithm measures the relative
angle between any two adjacent hits (see Figure 8.5). After compensating for noise in the readings
(caused by the inaccuracies in position between adjacent hits), the angle histogram shown in Figure
8.6a can be built. The uniform direction of the main walls are clearly visible as peaks in the angle
histogram. Computing the histogram modulo % results in only two main peaks: one for each pair of
parallel walls. This algorithm is very robust with regard to openings in the walls, such as doors and
windows, or even cabinets lining the walls.