Transcript Semantic Navigation Maps for Mobile Robot Localization on Planetary Surfaces Gregor Jochmann RIF e.V. Department Robot Technology Joseph-von-Fraunhofer-Str.

Semantic Navigation Maps for Mobile Robot
Localization on Planetary Surfaces
Gregor Jochmann
RIF e.V.
Department Robot Technology
Joseph-von-Fraunhofer-Str. 20
D-44227 Dortmund
Contents
■
■
■
■
■
Motivation
Map Concept
Map Generation
Map Utilization
Applications
ASTRA 2013, Noordwijk, The Netherlands. Gregor Jochmann, RIF, Department Robot Technology
2
Motivation
VEROSIM – The Virtual Robotics Testbed
ASTRA 2013, Noordwijk, The Netherlands. Gregor Jochmann, RIF, Department Robot Technology
4
VEROSIM – The Virtual Robotics Testbed
ASTRA 2013, Noordwijk, The Netherlands. Gregor Jochmann, RIF, Department Robot Technology
5
Motivation
■ The Localization Problem
model
pose
sensor data
localization
[map]
[map]
■ Possible application areas
ASTRA 2013, Noordwijk, The Netherlands. Gregor Jochmann, RIF, Department Robot Technology
6
Map Representation
ASTRA 2013, Noordwijk, The Netherlands. Gregor Jochmann, RIF, Department Robot Technology
7
Requirements
■ suitable for different
● environments
● localization algorithms
● navigation algorithms
■ consistent, human-comprehensible data representation
ASTRA 2013, Noordwijk, The Netherlands. Gregor Jochmann, RIF, Department Robot Technology
8
Map Concept
Key Aspects
■ landmark-based map
● per landmark
□
□
□
□
position
orientation (if applicable)
type-specific features
application-independent quality measure
■ digital elevation model
■ [path network]
■ rules to derive application-specific quality measures
Scenarioindependent map
Quality measures
Map
Scenario-specific
knowledge
ASTRA 2013, Noordwijk, The Netherlands. Gregor Jochmann, RIF, Department Robot Technology
10
Plugin Concept
ASTRA 2013, Noordwijk, The Netherlands. Gregor Jochmann, RIF, Department Robot Technology
11
Map Generation
Map Generation Overview
Rocks
Craters
Mountains
Shadow
segmentation
Edge detection
Search for DEM
maxima
Shadow ellipses
Edge Pairing
Topographical
characteristics
Landmark
detection
Crater ellipses
Semantic
abstraction
Perspective projection to global coordinates
Semantic abstraction
Plausibility tests and cleaning
Merging
of multiple
observations
Map
generation
Valuation of landmarks
ASTRA 2013, Noordwijk, The Netherlands. Gregor Jochmann, RIF, Department Robot Technology
13
Cleaning
■
ASTRA 2013, Noordwijk, The Netherlands. Gregor Jochmann, RIF, Department Robot Technology
14
Merging
■
ASTRA 2013, Noordwijk, The Netherlands. Gregor Jochmann, RIF, Department Robot Technology
15
Valuation
■
ASTRA 2013, Noordwijk, The Netherlands. Gregor Jochmann, RIF, Department Robot Technology
16
Map Utilization
Landmark Identification
■ mapping: observations vs.
landmarks in map
●
●
●
●
requirement for localization
positional information
landmark-specific features
robot pose information
■ benefit from of semantic
information:
● typed landmarks
● distance measure feature
weights
ASTRA 2013, Noordwijk, The Netherlands. Gregor Jochmann, RIF, Department Robot Technology
18
Localization Algorithms – Particle Filter
■
ASTRA 2013, Noordwijk, The Netherlands. Gregor Jochmann, RIF, Department Robot Technology
19
Localization Algorithms – Kalman Filters
■
ASTRA 2013, Noordwijk, The Netherlands. Gregor Jochmann, RIF, Department Robot Technology
20
Applications
Planetary Environments
ASTRA 2013, Noordwijk, The Netherlands. Gregor Jochmann, RIF, Department Robot Technology
22
Forest Environments
■ driver assistance
■ tree documentation
■ tree detection with laser scanners
and stereo cameras
■ additional sensors
■ simulated and real data
ASTRA 2013, Noordwijk, The Netherlands. Gregor Jochmann, RIF, Department Robot Technology
23