High-Precision Globally-Referenced Position and Attitude via a Fusion of Visual SLAM, Carrier-Phase-...
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![Page 1: High-Precision Globally-Referenced Position and Attitude via a Fusion of Visual SLAM, Carrier-Phase- Based GPS, and Inertial Measurements Daniel Shepard.](https://reader030.fdocuments.in/reader030/viewer/2022033100/56649cf85503460f949c90d0/html5/thumbnails/1.jpg)
High-Precision Globally-Referenced Position and Attitude via a Fusion of Visual SLAM,
Carrier-Phase-Based GPS, and Inertial Measurements
Daniel Shepard and Todd Humphreys
2014 IEEE/ION PLANS Conference, Monterey, CA | May 8, 2014
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Globally-Referenced Visual SLAM
Motivating Application: Augmented Reality
Estimation Architecture
Bundle Adjustment (BA)
Simulation Results for BA
Overview
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Produces high-precision estimates of Camera motion (with ambiguous scale for monocular SLAM) A map of the environment
Limited in application due to lack of a global reference
Stand-Alone Visual SLAM
[1] G. Klein and D. Murray, “Parallel tracking and mapping for small AR workspaces,” in 6th IEEE and ACM International Symposium on Mixed and Augmented Reality. IEEE, 2007, pp. 225–234.
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Globally-referenced solution if fiduciary markers are globally-referenced
Requires substantial infrastructure and/or mapping effort Microsoft’s augmented reality maps (TED2010[2])
Visual SLAM with Fiduciary Markers
[2] B. A. y Arcas, “Blaise Aguera y Arcas demos augmented-reality maps,” TED, Feb. 2010, http://www.ted.com/talks/blaise aguera.html.
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Can globally-referenced position and attitude (pose) be recovered
from combining visual SLAM and GPS?
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No GPS positions Translation Rotation Scale
Observability of Visual SLAM + GPS 1 GPS position
Translation Rotation Scale
2 GPS positions Translation Rotation Scale
~
3 GPS positions Translation Rotation Scale
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CDGPS anchors visual SLAM to a global reference frame
Can add an IMU to improve dynamic performance (not required!)
Can be made inexpensive
Requires little infrastructure
Combined Visual SLAM and CDGPS
Very Accurate!
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Augmenting a live view of the world with computer-generated sensory input to enhance one’s current perception of reality[3]
Current applications are limited by lack of accurate global pose
Potential uses in Construction Real-Estate Gaming Social Media
Motivating Application: Augmented Reality
[3] Graham, M., Zook, M., and Boulton, A. "Augmented reality in urban places: contested content and the duplicity of code." Transactions of the Institute of British Geographers.
.
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Sensors: Camera Two GPS antennas
(reference and mobile) IMU
How can the information from these sensors best be combined to estimate the camera pose and a map of the environment? Real-time operation Computational burden vs. precision
Estimation Architecture Motivation
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Sensor Fusion Approach
IMU
Visual SLAM CDGPS
Tighter coupling = higher precision, but increased computational burden
IMU
Visual SLAM CDGPS
IMU
Visual SLAM CDGPS
IMU
Visual SLAM CDGPS
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The Optimal Estimator
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IMU only for Pose Propagation
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Tightly-Coupled Architecture
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Loosely-Coupled Architecture
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Hybrid Batch/Sequential Estimator Only geographically diverse frames (keyframes) in batch estimator
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State Vector:
Measurement Models: CDGPS Positions:
Image Feature Measurements:
Bundle Adjustment State and Measurements
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Weighted least-squares cost function Employs robust weight functions to handle outliers
Sparse Levenberg-Marquart algorithm Computational complexity linear in number of point features, but
cubic in number of keyframes
Bundle Adjustment Cost Minimization
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Initialize BA based on stand-alone visual SLAM solution and CDGPS positions Determine similarity transform relating coordinate systems
Generalized form of Horn’s transform[4]
Rotation: Rotation that best aligns deviations from mean camera position
Scale: A ratio of metrics describing spread of camera positions
Translation: Difference in mean antenna position
Bundle Adjustment Initialization
[4] B. K. Horn, “Closed-form solution of absolute orientation using unit quaternions,” JOSA A, vol. 4, no. 4, pp. 629–642, 1987.
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Simulations investigating estimability included in paper
Hallway Simulation: Measurement errors:
2 cm std for CDGPS 1 pixel std for vision
Keyframes every 0.25 m 242 keyframes 1310 point features
Three scenarios:1. GPS available
2. GPS lost when hallway entered
3. GPS reacquired when hallway exited
Simulation Scenario for BA
A
B
C
D
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Simulation Results for BA
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Hybrid batch/sequential estimator for loosely-coupled visual SLAM and CDGPS with IMU for state propagation Compared to optimal estimator
Outlined algorithm for BA (batch)
Presented a novel technique for initialization of BA
BA simulations Demonstrated positioning accuracy of cm and attitude accuracy of
in areas of GPS availability
Attained slow drift during GPS unavailability (0.4% drift over 50 m)
Summary
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State Vector:
Propagation Step: Standard EKF propagation step using accelerometer and gyro
measurements
Accelerometer and gyro biases modeled as a first-order Gauss-Markov processes
More information in paper …
Navigation Filter
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Measurement Update Step: Image feature measurements from all non-keyframes
Temporarily augment the state with point feature positions Prior from map produced by BA Must ignore cross-covariances filter inconsistency
Similar block diagonal structure in the normal equations as BA
Navigation Filter (cont.)
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Simulation Results for BA (cont.)