Subterranean Sensing Research Overview 1. CMU Field Robotics Center Ultra-high density autonomous underground survey with FMCW Lidar on CaveCrawler robot.

Slides:



Advertisements
Similar presentations
Feature Based Image Mosaicing
Advertisements

CSE473/573 – Stereo and Multiple View Geometry
Research Services Georgia Baptist Convention Photography Basics - Composition Techniques to Becoming a Better Photographer
System Integration and Experimental Results Intelligent Robotics Research Centre (IRRC) Department of Electrical and Computer Systems Engineering Monash.
16421: Vision Sensors Lecture 6: Radiometry and Radiometric Calibration Instructor: S. Narasimhan Wean 5312, T-R 1:30pm – 2:50pm.
Global Security. OVERVIEW Discoverii represents day/night vision technology platform that combines an innovative, near infra-red, continuous-wave laser.
Remote sensing in meteorology
Dana Cobzas-PhD thesis Image-Based Models with Applications in Robot Navigation Dana Cobzas Supervisor: Hong Zhang.
Multi-Sensor Data Fusion H.B. Mitchell UNCLASSIFIED.
Jeff B. Pelz Visual Perception Laboratory Carlson Center for Imaging Science Rochester Institute of Technology Using Eyetracking to Improve Image Composition.
Copyright  Philipp Slusallek Cs fall IBR: Model-based Methods Philipp Slusallek.
Structured light and active ranging techniques Class 8.
Autonomous Landing Hazard Avoidance Technology (ALHAT) Page 1 March 2008 Go for Lunar Landing Real-Time Imaging Technology for the Return to the Moon Dr.
Introduction to Computer Vision CS223B, Winter 2005.
Graphics research and courses at Stanford
Computer Vision (CSE P576) Staff Prof: Steve Seitz TA: Jiun-Hung Chen Web Page
NVIDIA Lecture 10 Copyright  Pat Hanrahan Image-Based Rendering: 1st Wave Definition: Using images to enhance the realism of 3D graphics Brute Force in.
55:148 Digital Image Processing Chapter 11 3D Vision, Geometry Topics: Basics of projective geometry Points and hyperplanes in projective space Homography.
Basic Principles of Imaging and Photometry Lecture #2 Thanks to Shree Nayar, Ravi Ramamoorthi, Pat Hanrahan.
My Research Experience Cheng Qian. Outline 3D Reconstruction Based on Range Images Color Engineering Thermal Image Restoration.
Structured light and active ranging techniques Class 8
A Brief Overview of Computer Vision Jinxiang Chai.
Modeling And Visualization Of Aboriginal Rock Art in The Baiame Cave
1 Intelligent Robotics Research Centre (IRRC) Department of Electrical and Computer Systems Engineering Monash University, Australia Visual Perception.
Computer Graphics I, Fall 2008 Image Formation.
Computer Visualization BIM Curriculum 03. Topics  History  Computer Visualization Methods  Visualization Workflow  Technology Background.
Sensors. Sensors are for Perception Sensors are physical devices that measure physical quantities. – Such as light, temperature, pressure – Proprioception.
Real-Time High Resolution Photogrammetry John Morris, Georgy Gimel’farb and Patrice Delmas CITR, Tamaki Campus, University of Auckland.
Intelligent Robotics Group NASA Ames Research Center Intelligent Robotics Group NASA Ames Research Center Planning for the Mapping and Exploration of Human.
Automatic Registration of Color Images to 3D Geometry Computer Graphics International 2009 Yunzhen Li and Kok-Lim Low School of Computing National University.
Visual Perception PhD Program in Information Technologies Description: Obtention of 3D Information. Study of the problem of triangulation, camera calibration.
16421: Vision Sensors Lecture 7: High Dynamic Range Imaging Instructor: S. Narasimhan Wean 5312, T-R 1:30pm – 3:00pm.
Remotely Sensed Data EMP 580 Fall 2015 Dr. Jim Graham Materials from Sara Hanna.
Guilford County SciVis V Applying Pixel Values to Digital Images.
High-Resolution Interactive Panoramas with MPEG-4 발표자 : 김영백 임베디드시스템연구실.
Today’s Agenda 1.Scribbler Program Assignment 1.Project idea due next class 2.Program demonstration due Wednesday, June 3 2.Attendance & lab pair groupings.
16421: Vision Sensors Lecture 7: High Dynamic Range Imaging Instructor: S. Narasimhan Wean 5312, T-R 1:30pm – 3:00pm.
1 Digital Image Processing Dr. Saad M. Saad Darwish Associate Prof. of computer science.
Cmput412 3D vision and sensing 3D modeling from images can be complex 90 horizon 3D measurements from images can be wrong.
COMP322/S2000/L261 Geometric and Physical Models of Objects Geometric Models l defined as the spatial information (i.e. dimension, volume, shape) of objects.
MASKS © 2004 Invitation to 3D vision. MASKS © 2004 Invitation to 3D vision Lecture 1 Overview and Introduction.
Tutorial Visual Perception Towards Computer Vision
55:148 Digital Image Processing Chapter 11 3D Vision, Geometry Topics: Basics of projective geometry Points and hyperplanes in projective space Homography.
55:148 Digital Image Processing Chapter 11 3D Vision, Geometry Topics: Basics of projective geometry Points and hyperplanes in projective space Homography.
Image Perception ‘Let there be light! ‘. “Let there be light”
1Ellen L. Walker 3D Vision Why? The world is 3D Not all useful information is readily available in 2D Why so hard? “Inverse problem”: one image = many.
Data Preprocessing Dr Lewis RSU Geography UCL. Rationale when we view an RS image, we see a 'picture’
MASKS © 2004 Invitation to 3D vision. MASKS © 2004 Invitation to 3D vision Lecture 1 Overview and Introduction.
How the lens reflects and refracts light This is usually at the point where the light originates.
Microscopes Their types and uses.
Image Perception ‘Let there be light! ‘. “Let there be light”
Nov Visualization with 3D CG
Advanced Computer Graphics
From: Surface color perception and equivalent illumination models
System Overview CHRISS Combined High-Resolution Imaging and Spectroscopy System Intelligent Systems Group – University of Duisburg-Essen, Germany.
CS 4722 Computer Graphics and Multimedia Spring 2018
Digital Image Processing Introduction
Team A – Perception System using Stereo Vision and Radar
FUSION Software FUSION Data interface LDV Data viewer Sample options
Wrap-up Computer Vision Spring 2018, Lecture 28
What have we learned so far?
Image Based Modeling and Rendering (PI: Malik)
EMP 580 Fall 2015 Dr. Jim Graham Materials from Sara Hanna
Computer Vision (CSE 490CV, EE400B)
Distance Sensor Models
CMSC 426: Image Processing (Computer Vision)
Digital Image Fundamentals
Remote sensing in meteorology
Wrap-up Computer Vision Spring 2019, Lecture 26
Wrap-up and discussion
Presentation transcript:

Subterranean Sensing Research Overview 1

CMU Field Robotics Center Ultra-high density autonomous underground survey with FMCW Lidar on CaveCrawler robot Reconnaissance Automated Modeling

CMU Field Robotics Center Reconnaissance Automated Modeling

CMU Field Robotics Center Multi-Sensor Perception Super-Resolution Fusion Detail of rock wall, airflow curtain and roof supports in a mine.

CMU Field Robotics Center Visualization and Virtualization (Left) Point Rendering techniques produce solid appearance from dense point measurements using GPU acceleration. Scene of a mine corridor, 4 million points. (Bottom) Non-Photorealistic Rendering with exaggerated reflectance models enhances viewer understanding of scene geometry. Mine wall and ceiling detail, 300k polygons.

CMU Field Robotics Center Multispectral Imaging Near Infrared, 2. Infrared (Thermal), 3. Rectilinear color, 4. Fisheye Color, images in Walker’s Mill cave.

CMU Field Robotics Center Imaging in Darkness (Left) Long range borehole panoramic imager with intelligent illumination (Right) Structured Light geometry scanner projecting a beam pattern

CMU Field Robotics Center Imaging in Darkness Panorama stitching from multiple views Fusion of multiple exposures for optimal clarity warp align 1 2 blend Fisheye projection of potash mine panorama

CMU Field Robotics Center Calibration and Characterization Radiometric and Colorimetric Geometric

Stereo Vision Phase Shift Lidar Flash Lidar MMW Radar Structured Light Long Range Time of Flight Lidar Short Range Time-of-Flight Lidar Range Sensors CMU Field Robotics Center

Digital SLRs Bridge Cameras Near IR/LowLight Industrial/MV Security/IP Thermal/IR CMU Field Robotics Center Imaging Sensors

Feedback: Privacy Policy Feedback
Do Not Sell My Personal Information
About project: SlidePlayer Terms of Service

© 2024 SlidePlayer.com Inc. All rights reserved.