Master Thesis - Hochschule Bonn-Rhein-Sieg

9. List of Figures Master Thesis Björn Ostermann page 124 of 126
Figure 36: Retrieving data from the Data Exchange Object ................................................................. 57
Figure 37: Sample images, taken in the workplace a) reflection image and b) distance image (see also
Figure 21) .................................................................................................................... 59
Figure 38: Scaling algorithm a) to b) with a small used range and c) to d) with a large used range..... 61
Figure 39: Histogram equalization Redistribution of the greyscale values (a, b), based on their
probability distribution function (c)............................................................................. 62
Figure 40: Reflection data, collected in a sample office environment a) unmodified, b) scaled
greyscale values, c) histogram analysis ....................................................................... 64
Figure 41: Converting 0 to 7.5m into colour......................................................................................... 64
Figure 42: Modelling distance to colour in five steps along the edge of the RGB colour space RGB
cube image taken from [http://en.wikipedia.org/wiki/RGB_color_space] .................. 65
Figure 43: Application of a Kalman filter on consecutive sensor data – images created in collaboration
with [81] ...................................................................................................................... 67
Figure 44: The value matrix has to be transformed from the original state (blue) to the smoothened
state (yellow) – image created in collaboration with [81] ........................................... 68
Figure 45: Applying a Gaussian matrix to get the result of one element – image created in
collaboration with [81] ................................................................................................ 68
Figure 46: Gaussian matrix, build by multiplying a Gaussian vector with its transpose – image created
in collaboration with [81] ............................................................................................ 69
Figure 47: a) Converting the original matrix (blue) to a temporary matrix (green) b) and converting the
temporary matrix into the result matrix (yellow) – images created in collaboration with
[81] .............................................................................................................................. 69
Figure 48: Fluctuation noise in background-foreground comparison as shown in Figure 24 ............... 70
Figure 49: Fluctuation noise after the offset filter (12 cm) as shown in Figure 24 ............................... 72
Figure 50: Intrusion map, red = no intrusion, dark green = intrusion, light green = noise a) before
filtering b)
spike filter, threshold 1 c) spike filter, threshold 2 d) spike filter, threshold 3 73
Figure 51: a) probability filter size 1, b) probability filter size 2 Intrusion map, red = no intrusion, dark
green = intrusion, light green = noise .......................................................................... 74
Figure 52: Fluctuation noise after the offset filter (12 cm) and plausibility filter as shown in Figure 24
..................................................................................................................................... 75
Figure 53: a) Creating the virtual robot base, blue circle: robot base radius, blue cross: centre of robot
base, red square: not robot base, green square: robot base b) Virtual base, added in the
program (see Figure 26) .............................................................................................. 75
Figure 54: Sample region ...................................................................................................................... 77
Figure 55: Region outlining algorithm a) finding the first pixel b) direction of search c) to e) finding
the next pixel f) finished outline.................................................................................. 78
Figure 56: Filling principle.................................................................................................................... 79
Figure 57: Filling from above and below.............................................................................................. 80
Figure 58: Remaining objects after region size filter ............................................................................ 81
Figure 59: Robot region identified by its base’s centre (blue cross)..................................................... 81
Figure 60: Detecting collisions by region history ................................................................................. 83
Figure 61: Robot region (green) increased until it touches the (red) intruding person a) scene from the
video on CD, picture shows live scene picture in picture shows region enlargement
overlapped with intensity values, shown on PC b) enlarged robot and object only, as
observed by the 3D camera and shown on the PC....................................................... 84
Figure 62: Region growing algorithm dark green: robot’s original region light green: temporary region
red: intruding object..................................................................................................... 85