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LiDAR feature extraction
1.
Feature Extraction Conor
Mc Elhinney Monday 14th February 2011
2.
3.
Road extraction
4.
Roadside feature
extraction
5.
6.
Road extraction
7.
Roadside feature
extraction
8.
9.
high accuracy
( > 90%)
10.
easy verification
/ validation in software (manual)
11.
12.
Road extraction
13.
Roadside feature
extraction
14.
15.
automated road
edge error removal / processing
16.
17.
automated road
edge error removal / processing
18.
19.
20.
Select nav
points from db
21.
Select those
nav points separated by a specific interval1m
22.
23.
Select nav
points from db
24.
Select those
nav points separated by a specific interval
25.
Can create cross
sections orthogonal to these nav points and the vans heading1m
26.
27.
Select nav
points from db
28.
Select those
nav points separated by a specific interval
29.
Can create cross
sections orthogonal to these nav points and the vans heading
30.
Take a
cross section1m
31.
32.
Select nav
points from db
33.
Select those
nav points separated by a specific interval
34.
Can create cross
sections orthogonal to these nav points and the vans heading
35.
Take a
cross section
36.
37.
Select nav
points from db
38.
Select those
nav points separated by a specific interval
39.
Can create cross
sections orthogonal to these nav points and the vans heading
40.
Take a
cross section
41.
Find edge
based on slope
42.
43.
Select nav
points from db
44.
Select those
nav points separated by a specific interval
45.
Can create cross
sections orthogonal to these nav points and the vans heading
46.
Take a
cross section
47.
Find edge
based on slope
48.
Refine based
on pulse width and amplitude
49.
50.
Select nav
points from db
51.
Select those
nav points separated by a specific interval
52.
Can create cross
sections orthogonal to these nav points and the vans heading
53.
Take a
cross section
54.
Find edge
based on slope
55.
Refine based
on pulse width and amplitude
56.
Return
edgesIterate for all nav points
57.
58.
Select nav
points from db
59.
Select those
nav points separated by a specific interval
60.
Can create cross
sections orthogonal to these nav points and the vans heading
61.
Take a
cross section
62.
Find edge
based on slope
63.
Refine based
on pulse width and amplitude
64.
Return
edgesIterate for all nav points
65.
66.
Select nav
points from db
67.
Select those
nav points separated by a specific interval
68.
Can create cross
sections orthogonal to these nav points and the vans heading
69.
Take a
cross section
70.
Find edge
based on slope
71.
Refine based
on pulse width and amplitude
72.
Return
edgesIterate for all nav points
73.
74.
75.
Regional road
containing 2-3 lanes.
76.
Left edge
in results is excellent
77.
Right edge
is quite good.
78.
lower density
79.
more obstructions
(cars / islands)
80.
81.
Road Edges –
300m straight
82.
Road Edges –
300m straight
83.
Road Edges –
Bend
84.
Road Edges –
Bend
85.
Road Edges –
layby
86.
Road Edges –
layby
87.
Road Edges –
Roundabout
88.
Road Edges –
Roundabout
89.
Road Edges –
Multilane with layby
90.
Road Edges –
Multilane with layby
91.
Road Edges –
Right Edge not perfect
92.
Road Edges –
Right Edge not perfect
93.
Road Surface
94.
Road Surface
95.
Road Surface
96.
Road Surface
97.
98.
Need to integrate
error processing of right edge into the algorithm.
99.
Accuracy assessment
of edges
100.
Quantitative assessment
of road edge algorithms doesn’t exist. We intend to develop a line comparison based approach as point based comparison involves too much error.
101.
102.
Road extraction
103.
Roadside feature
extraction
104.
105.
Automate pole
extraction
106.
Automate tree
extraction
107.
Determine viability
of this approach to other roadside features.
108.
109.
Automate pole
extraction
110.
Automate tree
extraction
111.
Determine viability
of this approach to other roadside features.
112.
113.
114.
Take a
cross section (50m x 20m)
115.
116.
Take a
cross section (50m x 20m)
117.
Cluster data
using region growing
118.
119.
Take a
cross section (50m x 20m)
120.
Cluster data
using region growing
121.
Remove “ground” region
122.
123.
Take a
cross section (50m x 20m)
124.
Cluster data
using region growing
125.
Remove “ground” region
126.
Process segments into
grouped objectsIterate for all cross sections in survey
127.
128.
129.
Pole extraction examples
– lights
130.
Pole extraction examples
– signpost
131.
Pole extraction examples
– signpost
132.
Pole extraction examples
– Telegraph pole
133.
Pole extraction examples
– Telegraph pole
134.
Pole extraction examples
– Signs with two pole bases
135.
Pole extraction examples
– Signs with two pole bases
136.
137.
138.
139.
140.
The initial
classification of roadside objects works very well.
141.
We need
to develop new extractors/detectors for linear features like walls/fences and so on.
142.
143.
The initial
classification of roadside objects works very well.
144.
We need
to develop new extractors/detectors for linear features like walls/fences and so on.
145.
The initial
results of the object finder are very promisingWall
146.
Other features –
Fences / walls/ barriers Crash barrier
147.
Other features –
Fences / walls/ barriers Fence
148.
Other features –
Fences / walls/ barriers
149.
Other features –
Fences / walls/ barriers Wall
150.
Other features –
Fences / walls/ barriers Crash barrier
151.
Other features –
Fences / walls/ barriers Fence
152.
Other features –
Bridges
153.
Other features –
Bridges
154.
155.
Once this
is complete we can work on pole classification, i.e differentiate between signs, lightposts, telegraph poles....
156.
157.
Road extraction
158.
Roadside feature
extraction
159.