Navigation and Mapping for Autonomous Satellite Servicing

Abstract

Autonomous operations in space, such as in-space servicing, Assembly, and manufacture (ISAM) require spacecraft being able to navigate autonomously with respect to other vehicles and in unknown environments. An important step in this direction is for vehicles to be able to Simultaneously Localize and Map (SLAM) themselves in their surroundings, which is for them to be able to save the location of important features observed by the sensors on the vehicle while also estimating the position of the vehicle with respect to some frame of reference. This report presents a novel modified method of Robocentric EKF-SLAM which includes a second order propagation before every update and maintains filter consistency across challenging scenarios. The filter was tested in simulation, a real world dataset as well as through experiments on a vehicle in the lab to demonstrate the efficacy of this SLAM method. This contribution could help make ISAM using an autonomous spacecraft safer and more robust in the future.

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Document Details

Document Type
Technical Report
Publication Date
Aug 29, 2023
Accession Number
AD1214724

Entities

People

  • Renato Zanetti

Organizations

  • University of Texas at Austin

Tags

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Artificial Satellites
  • Cartography
  • Collision Avoidance
  • Computational Complexity
  • Consistency
  • Data Sets
  • Kalman Filters
  • Maps
  • Navigation
  • Simulations
  • Simultaneous Localization And Mapping
  • Spacecraft
  • Three Dimensional
  • World Geodetic System

Readers

  • Computer Vision.
  • Unmanned Aerial System (UAS) Autonomous Capabilities and Mission Reconnaissance.

Technology Areas

  • Space
  • Space - Spacecraft Maneuvers