This work presents an approach to utilize high resolution surface data as a-priori information for three dimensional path planning at very low altitude. The major challenge is preserve features while reducing the amount of data to a minimum. Non significant height points are eliminated by a neighbor search, performed within a data structure generated from pseudo 3D Delaunay meshing. A comparison to an alternatively implemented simplification shows which inherent building features can be preserved. For highlighting the feasibility of the approach, the processing results of real urban surface data from the inner city of Berlin is presented and used for sampling based path planning of an unmanned helicopter.

1. Meshing and Simplification of High Resolution Urban
Surface Data for UAV Path Planning
Florian Adolf1), Heiko Hirschmüller2)
1) Institute of Flight Systems, Unmanned Aircraft Department, German Aerospace Center (DLR), Braunschweig, Germany
2) Institute of Robotics and Mechatronics, Perception and Kognition Department, DLR, Oberpfaffenhofen, Germany

2. Meshing and Simplification of High Resolution Urban Surface Data for UAV Path Planning > F.Adolf et..al. > UAV 2010 > 23rd June 2010
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Motivation Context
Flying UAVs in urban environments is of high interest:
e.g. surveillance, reconnaissance or rescue missions
Haiti Earthquake of 2010: Ruins in Léogâne, close to the earthquake epicentre
Source: Wikipedia

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Problem: Automated Path Planning Database
Precise obstacle database for collision free waypoint navigation
High resolution surface data can be acquired, BUT:
Can be too detailed for 3D planning with limited computational
resources on board small, light weight UAVs
However, important obstacle features must be preserved
Example: High Resolution Surface Model of Berlin

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Acquisition of highly detailed a Digital Surface Model (DSM):
Aerial or satellite imagery
Utilize DSM as a-priori information in path planner
Meshing of DSM points
Reduce mesh with conservative method
Approach Outline

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Capture images of classical
aerial push broom or full frame
cameras
Can equally be generated from
satellite imagery
Overlapping images are
matched by the Semi-Global
Matching (SGM)
Note: Whole processing chain
works fully automatic
Initial DSM Acquisition

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Surface Meshing
vertices:10201 faces: 20000
conversion time: 0.969 sec100x100m² area, 1x1x0.1m³ regular grid
Initial Mesh Example

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Step 1: Horizontal plane simplification
Step 2: Co-linear plane simplification
Mesh Simplification
2-Step Processing

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conversion time: 1.093 sec
vertices:5870 (-42%) faces: 11338
Mesh Simplification
Reduced Mesh: Urban Example

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DSM Sampling + Scaling
XY-3 Delaunay Meshing
Find Co-planar-Z Neighbor Faces
Find Co-linear Neighbor Faces
Remove Face Vertices Found
Small Wrap-Up
From (any) DSM to post processed meshes

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Application: Roadmap-based Path Planner
Example: Inner City of Berlin
1. Acquire DSM

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Application: Roadmap-based Path Planner
Example: Inner City of Berlin
1. Acquire DSM
2. Mesh Points
1:1 sampling of 1750x1400m² yields 417279 kB triangle faces in polygon set

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Application: Roadmap-based Path Planner
Example: Inner City of Berlin
1. Acquire DSM
2. Mesh Points
3. Simplify Mesh
Simplification yields 158311 kB triangle faces in polygon set
-62% vertices

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Application: Roadmap-based Path Planner
Example: Inner City of Berlin
1. Acquire DSM
2. Mesh Points
3. Simplify Mesh
4. Build Roadmap
BerlinFrame_06264_part_dom_3-1-simplified-potsdamer.poly
Zoomed in: Potsdamer Platz area
(VTOL) UAV
start
position

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Application: Roadmap-based Path Planner
Example: Inner City of Berlin
1. Acquire DSM
2. Mesh Points
3. Simplify Mesh
4. Build Roadmap
BerlinFrame_06264_part_dom_3-1-simplified-potsdamer.poly
QRM-Hammersley sampling (1465
samples)
Roadmap build time 28.656 sec
Zoomed in: Potsdamer Platz area
Free space graph
(Roadmap)

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Application: Roadmap-based Path Planner
Example: Inner City of Berlin
1. Acquire DSM
2. Mesh Points
3. Simplify Mesh
4. Build Roadmap
5. Plan mission(s)
BerlinFrame_06264_part_dom_3-1-simplified-potsdamer.poly
Zoomed in: Potsdamer Platz area
Polygons loaded in 1.7 sec
World Size 496m*548m*122.4m (WxLxH)
Roadmap built in 25.609 sec
Mission planned in 0.672 sec
Berlin, Potsdamer Platz

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Application: Roadmap-based Path Planner
Example: Inner City of Berlin
1. Acquire DSM
2. Mesh Points
3. Simplify Mesh
4. Build Roadmap
5. Plan mission(s)
BerlinFrame_06264_part_dom_3-1-simplified-potsdamer.poly
Full test area
Berlin, Potsdamer Platz
World Model loaded in 96.1 sec
Roadmap build time 122.5 sec
Mission planned in 5.3 sec

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Comparison of Planning Times
Planning Performance Increase Examples

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Summary
Integrated approach to acquire high resolution DSM and use it for 3D path
planning
DSM data converted into polygonal surface mesh to perform graph-based
simplification
Examples for reduced path planning times in urban terrain shown
(here: Berlin city)

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Questions?
Thank You!
florian.adolf@dlr.de

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BACKUP

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Problematische
Regionen (z.B. “dünne”
Wände) bleiben
erhalten
vertices:27173 (-75.9905%) (initially:113176)
Ebene Flächen stark
reduziert (± 0.4m )
Enge Passagen,
wie z.B.
Innenhöfe,
erhalten und ohne
“Geister”

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Berlin Mitte: Processing Log
covered area: 1.750x1.400m²
vertices initially: 2.450.000
vertices: 944.305 (-61.4569%)
vertices deleted: 1.505.695
co-planar-z marked: 1.481.411
co-linear marked: 24.284
faces: 1.882.338
infinite: 6.270
processing time: 4:45 min

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Edge Costs: Shortest

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Edge Costs: Low Flight
Euklid. Distanz