Scene Modeling from Motion-Free Radar Sensing Alex Foessel - - PowerPoint PPT Presentation

Scene Modeling from Motion-Free Radar Sensing

Alex Foessel Robotics Institute Carnegie Mellon University Ph.D. Thesis Proposal May 13, 1999

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Motivation

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Presentation

I. Research on Radar for Robots II. Work to Date

• III. Radar Issues
• IV. Technical Approach

V. Research Plan

• VI. Summary

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Related Work

1.

• M. Lange and J. Detlefsen, “94 GHz Three-Dimensional

Imaging Radar Sensor for Autonomous Vehicles,” 1991. 2.

• D. Langer, “Proposal for an Integrated MMW Radar

System for Outdoor Navigation,” 1996. 3. U.S Patent No. 5,668,739 "System and Method for Tracking Objects Using a Detection System", 1997. 4.

• S. Clark and H. Durran-Whyte, “Autonomous Land

Vehicle Navigation Using Millimeter Wave Radar,” 1998. 5.

• S. Boehmke, J. Bares, E. Mutschler, K. Lay, "A High

Speed 3-D Radar Scanner for Automation," 1998.

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Work to Date

• Mechanically-scanning radar unit characterization

(experimental estimation of range accuracy, range resolution and beamwidth)

• Motion-free scanning-antennas study

(reveals specific issues for radar interpretation)

• 2-D evidence-grid implementation

(radar data merging tool, shows noise reduction)

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RAdio Detection And Ranging

• Millimeter-wave band (4 mm)
• Range of interest from 1 to 100 m

Range

Am plitude

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Wide-Beam Scene Sensing

• Large footprint --> combination of echoes
• Three-dimensional modeling

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Experimental Range Resolution

• Range resolution limited to two range intervals

6 8 4 10 2 12 Range (m)

Targets 2.0 m apart 1.5 m apart 0.5 m apart 1.0 m apart

6 8 4 10 2 12 Range (m) 6 8 4 10 2 12 Range (m) 6 8 4 10 2 12 Range (m)

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Antenna-Radiation Pattern

• Beamwidth less than 10 degrees
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90

Schematic

Angle (deg)

Graph Representation Representation

Amplitude

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Experimental Beamwidth

• Experimental estimation of beamwidth

−5 −4 −3 −2 −1 1 2 3 4 5 100 200 300 400 500 600 Signal Strength at 6.5 meters from Radar Signal Strength (no units) Angle (degrees)

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1 2 3 4 5 Angle (degrees)

Signal Strength

......

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Multilobe Angular Ambiguity

Range

A m p l i t u d e

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Motion-Free Scanning Antenna

• Electronic scanning of the beam
• Provides size reduction and reliability improvement

X-Scan Angle (deg) Y-Scan (deg)

Amplitude

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15 30

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15

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Am plitude

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15 3

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15

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X-Scan (deg) X-Scan (deg) Y

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c a n ( d e g )

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Motion-Free Scanning Antenna Varying Radiation Pattern

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Scanning angle (deg) Amplitude Scanning angle = -17 deg Scanning angle = +11 deg

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Specular Reflection

• Fails detection of specular surface
• Potential false target detection

Range

Am plitude

(a) (a)

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Radar-Issues Summary

• Large footprint --> combination of targets
• Multilobe sensing --> angular ambiguity
• Motion-free scanning antenna --> varying properties
• Specular surfaces --> missing surface and false target
• Orientation and material determine radar energy return

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Technical Approach

• Learn radar-signal heuristics
• Represent multilobe-sensor profile
• Enable varying profile capability
• Model surface reflectance and orientation
• Integrate in evidence grids appropriate for radar

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Identification of Planes

• Initial indication of a diffuse reflection plane

Spread Range A B C

Radar Sensing a Plane

Resulting Return-Energy Vector

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Experimental Range Accuracy

• Indicates accuracy can improve over range interval

Target at 10.5 m Target at 10.6 m

9 10 11 12 8

Amplitude Range (meters)

9 10 11 12 8

Amplitude Range (meters)

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Improvement of Range Accuracy

• Target at 10.6 m
• Max. value technique estimates 10.50 m
• Proposed technique estimates 10.58 m

9 10 11 12 8

Amplitude Range (meters)

Experimental Data

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Evidence Grids, A Convenient Representation

• Probabilistic scene representation for noisy sensors
• Works for wide-beam sensors
• Allows extension to multilobe profiles
• Facilitates representation of additional hypotheses

(specifically, orientation and reflectance)

• Representation commonly used for robotic tasks

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Evidence-Grid Implementation

• Data merging results in noise attenuation

Convex Obstacle Concave Obstacle Sensor Locations Evidence Grid

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Angular Ambiguity Resolution

• Successive observations resolve angular ambiguity.

(A) (B)

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Multilobe-Sensor Profile

• Sensor profile represents sensor geometry

Evidence Grid

Schematic Sensor-Profile Geometry Representation

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Varying Radiation-Pattern

−32 −40 −24 −28 −36 Azimuth (degrees) Azimuth (degrees)

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−8 −4

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Evidence Grid Evidence Grid

Amplitude Amplitude

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Surface Orientation and Reflectance

• Sensor profile updates additional hypotheses

Range Amplitude High Evidence Evidence of diffuse reflection Negative evidence

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Surface Model Enhancement

• Global approach for surface modeling

E E E E E U U U

Surface Model Grid values (orientation) (roughness)

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Surface Model for Improved Interpretation

• Known surface properties indicate stronger evidence

Evidence Grid Schematic Sensor-Profile Geometry Representation

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Interpretation Process

1. Obtain radar data 2. Apply radar-data heuristics 3. Build sensor profile 4. Update evidence grid 5. Model surface reflectivity and roughness 6. Iterate...

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Research Plan

1. Radar-interpreter design (Jun–Jul 1999) 2. Radar-data heuristics development (Aug–Sept 1999) 3. Radar-sensor profile development (Oct–Nov 1999) 4. Surface-extraction development (Dec 1999–Jan 2000) 5. Integration of interpreter components (Feb–Mar 2000) 6. Interpreter evaluation (Apr– May 2000) 7. Dissertation composition, presentation (Jun–Aug 2000)

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Summary

I. Previous Radar Developments for Robotics II. Work to Date (radar unit characterization, motion-free scanning antenna research, 2-D evidence grid)

• III. Radar Shortcomings (footprint, sidelobes, reflections,

varying radiation pattern)

• IV. Technical Approach (heuristics, evidence grids,

multilobe-sensor profile, surface modeling) V. Research plan

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Questions and Answers

?

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