Multi-Modal Localization for Autonomous Lunar Lander Robert Fisher - - PowerPoint PPT Presentation

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multi modal localization for autonomous lunar lander
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Multi-Modal Localization for Autonomous Lunar Lander Robert Fisher - - PowerPoint PPT Presentation

Mar 02, 2024 815 likes •983 views

Multi-Modal Localization for Autonomous Lunar Lander Robert Fisher Heather Jones (NOT TO SCALE) Fairing First Stage Separation Separation De-orbit and Braking Launch Descent Trans- Lunar Injection Earth Orbit Lunar Orbit Lunar

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SLIDE 1

Multi-Modal Localization for Autonomous Lunar Lander

Robert Fisher Heather Jones

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SLIDE 2

Launch Lunar Orbit Trans- Lunar Injection Earth Orbit Fairing Separation First Stage Separation Second Stage Separation Lunar Orbit Insertion De-orbit and Braking Descent

(NOT TO SCALE)

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SLIDE 3

Sensors

3

  • Inertial Measurement Unit
  • Star Tracker
  • Radio Telemetry
  • Radar Altimeter
  • Point Lidar(s)
  • Downlooking Camera(s)
  • Flash Lidar
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SLIDE 4

Sensors

4

  • Inertial Measurement Unit
  • Star Tracker
  • Radio Telemetry
  • Radar Altimeter
  • Point Lidar(s)
  • Downlooking Camera(s)
  • Flash Lidar
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SLIDE 5

Sensors

  • Inertial Measurement Unit
  • Star Tracker
  • Radio Telemetry
  • Radar Altimeter
  • Point Lidar(s)
  • Downlooking Camera(s)
  • Flash Lidar

5

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SLIDE 6

Sensors

6

  • Inertial Measurement Unit
  • Star Tracker
  • Radio Telemetry
  • Radar Altimeter
  • Point Lidar(s)
  • Downlooking Camera(s)
  • Flash Lidar
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SLIDE 7

Sensors

7

  • Inertial Measurement Unit
  • Star Tracker
  • Radio Telemetry
  • Radar Altimeter
  • Point Lidar(s)
  • Downlooking Camera(s)
  • Flash Lidar
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SLIDE 8

Sensors

8

  • Inertial Measurement Unit
  • Star Tracker
  • Radio Telemetry
  • Radar Altimeter
  • Point Lidar(s)
  • Downlooking Camera(s)
  • Flash Lidar
slide-9
SLIDE 9

Sensors

  • Inertial Measurement Unit
  • Star Tracker
  • Radio Telemetry
  • Radar Altimeter
  • Point Lidar(s)
  • Downlooking Camera(s)
  • Flash Lidar
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SLIDE 10

Dataset

  • Lunar Reconnaissance Orbiter
  • Imagery (with latitude/longitude

bounding box)

  • Laser altimeter data (with

latitude/longitude coordinates of each point)

  • Digital Elevation Map of the entire moon

constructed from laser altimeter data (118m x 118m per pixel)

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SLIDE 11

Multiple modes of data for one solution

  • (Blum, Mitchell ’98) introduced ‘co-training’

for semi-supervised learning.

  • Co-training principles apply in a variety of

applications which are not semi-supervised

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SLIDE 12

When to use multiple views?

  • We need to be able to localize using each view

independently.

  • Each localizer needs to give a comparable

confidence measure (i.e. variance of the distributions)

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SLIDE 13

When to use multiple views?

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SLIDE 14

Relation to previous work

  • Previous work (Singh, Lim, 2008) showed that

using altimetry combined with imagery performs better than either mode alone.

  • We wish to investigate additional modes,

perhaps multiple different views extracted from image data.

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SLIDE 15

References

  • Avrim Blum, Tom Mitchell. “Combining

labeled and unlabeled data with co-training”, COLT 1998.

  • Leena Singh, Sungyung Lim. “On Lunar on-
  • rbit Vision-Based Navigation: Terrain

Mapping, Feature Tracking driven EKF”, AIAA Guidance 2008.