Patron-Perez et al., 2015 - Google Patents
A spline-based trajectory representation for sensor fusion and rolling shutter camerasPatron-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 …
- 238000005096 rolling process 0 title abstract description 42
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in preceding groups
- G01C21/10—Navigation; Navigational instruments not provided for in preceding groups by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in preceding groups by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in preceding groups
- G01C21/20—Instruments for performing navigational calculations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T15/00—3D [Three Dimensional] image rendering
- G06T15/10—Geometric effects
- G06T15/20—Perspective computation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
- G06T3/0068—Geometric 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T13/00—Animation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO 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
- G01S5/00—Position-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 |