Image-Aided Navigation Using Cooperative Binocular Stereopsis

Abstract

This thesis proposes a novel method for cooperatively estimating the positions of two vehicles in a global reference frame based on synchronized image and inertial information. The proposed technique - cooperative binocular stereopsis - leverages the ability of one vehicle to reliably localize itself relative to the other vehicle using image data which enables motion estimation from tracking the three dimensional positions of common features. Unlike popular simultaneous localization and mapping (SLAM) techniques, the method proposed in this work does not require that the positions of features be carried forward in memory. Instead, the optimal vehicle motion over a single time interval is estimated from the positions of common features using a modified bundle adjustment algorithm and is used as a measurement in a delayed state extended Kalman filter (EKF). The developed system achieves improved motion estimation as compared to previous work and is a potential alternative to map-based SLAM algorithms.

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Document Details

Document Type
Technical Report
Publication Date
Mar 27, 2014
Accession Number
ADA604422

Entities

People

  • Justin T. Soeder

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • C4I
  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Air Force
  • Autonomous Navigation
  • Autonomous Systems
  • Computational Complexity
  • Computer Stereo Vision
  • Computer Vision
  • Image Processing
  • Kalman Filtering
  • Kalman Filters
  • Mathematical Filters
  • Measurement
  • Operating Systems
  • Simultaneous Localization And Mapping
  • Three Dimensional
  • Two Dimensional
  • United States Government
  • Wireless Communications

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computer Vision.
  • Inertial Navigation Systems.