[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Patron-Perez et al., 2015 - Google Patents

A spline-based trajectory representation for sensor fusion and rolling shutter cameras

Patron-Perez et al., 2015

Document ID
15486490354374284522
Author
Patron-Perez A
Lovegrove S
Sibley G
Publication year
Publication venue
International Journal of Computer Vision

External Links

Snippet

The use of multiple sensors for ego-motion estimation is an approach often used to provide more accurate and robust results. However, when representing ego-motion as a discrete series of poses, fusing information of unsynchronized sensors is not straightforward. The …
Continue reading at link.springer.com (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/20Analysis of motion
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in preceding groups
    • G01C21/10Navigation; Navigational instruments not provided for in preceding groups by using measurements of speed or acceleration
    • G01C21/12Navigation; Navigational instruments not provided for in preceding groups by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16Navigation; Navigational instruments not provided for in preceding groups by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10032Satellite or aerial image; Remote sensing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00Three dimensional [3D] modelling, e.g. data description of 3D objects
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00Navigation; Navigational instruments not provided for in preceding groups
    • G01C21/20Instruments for performing navigational calculations
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T15/003D [Three Dimensional] image rendering
    • G06T15/10Geometric effects
    • G06T15/20Perspective computation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
    • G06T3/0068Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image for image registration, e.g. elastic snapping
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T13/00Animation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01CMEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C11/00Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations

Similar Documents

Publication Publication Date Title
Patron-Perez et al. A spline-based trajectory representation for sensor fusion and rolling shutter cameras
Lovegrove et al. Spline Fusion: A continuous-time representation for visual-inertial fusion with application to rolling shutter cameras.
Jinyu et al. Survey and evaluation of monocular visual-inertial SLAM algorithms for augmented reality
CN109084732B (en) Positioning and navigation method, device and processing equipment
Panahandeh et al. Vision-aided inertial navigation based on ground plane feature detection
Newcombe et al. Kinectfusion: Real-time dense surface mapping and tracking
RU2713611C2 (en) Three-dimensional space simulation method
Hinzmann et al. Mapping on the fly: Real-time 3D dense reconstruction, digital surface map and incremental orthomosaic generation for unmanned aerial vehicles
Jaegle et al. Fast, robust, continuous monocular egomotion computation
Heo et al. Consistent EKF-based visual-inertial navigation using points and lines
US20210225012A1 (en) System and method for egomotion estimation
He et al. Relative motion estimation using visual–inertial optical flow
Spacek et al. Instantaneous robot self-localization and motion estimation with omnidirectional vision
Irmisch et al. Simulation framework for a visual-inertial navigation system
Laskar et al. Robust loop closures for scene reconstruction by combining odometry and visual correspondences
Gui et al. Robust direct visual inertial odometry via entropy-based relative pose estimation
Arndt et al. Do planar constraints improve camera pose estimation in monocular slam?
Li et al. Color-introduced frame-to-model registration for 3d reconstruction
CN117629241B (en) Multi-camera visual inertial odometer optimization method based on continuous time model
Xiang SLAM for Ground Robots: Theories and Applications
Masher Accurately scaled 3-D scene reconstruction using a moving monocular camera and a single-point depth sensor
Hashimoto et al. Self-localization from a 360-Degree Camera Based on the Deep Neural Network
Soliman et al. GPS-Enhanced RGB-D-IMU Calibration for Accurate Pose Estimation
Hettiarachchi Reconstruction of 3D Environments from UAV’s Aerial Video Feeds
He 3d reconstruction from passive sensors