Airborne smoothing and mapping using vision and inertial sensors

Authors:
Mitch Bryson;Matthew Johnson-Roberson;Salah Sukkarieh
Affiliations:
Australian Centre for Field Robotics, University of Sydney, NSW, Australia;Australian Centre for Field Robotics, University of Sydney, NSW, Australia;Australian Centre for Field Robotics, University of Sydney, NSW, Australia
Venue:
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Year:
2009

Citing 7
Cited 0

Marching cubes: A high resolution 3D surface construction algorithm

SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
A volumetric method for building complex models from range images

SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
An Approximate Minimum Degree Ordering Algorithm

SIAM Journal on Matrix Analysis and Applications
LAPACK Users' guide (third ed.)

LAPACK Users' guide (third ed.)
Multi Viewpoint Stereo from Uncalibrated Video Sequences

ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Visual Modeling with a Hand-Held Camera

International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a framework for integrating sensor information from an Inertial Measuring Unit (IMU), Global Positioning System (GPS) receiver and monocular vision camera mounted to a low-flying Unmanned Aerial Vehicle (UAV) for building large-scale terrain reconstructions. Our method seeks to integrate all of the sensor information using a statistically optimal non-linear least squares smoothing algorithm to estimate vehicle poses simultaneously to a dense point feature map of the terrain. A visualisation of the terrain structure is then created by building a textured mesh-surface from the estimated point features. The resulting terrain reconstruction can be used for a range of environmental monitoring missions such as invasive plant detection and biomass mapping.