Cabrera-Ponce et al., 2023 - Google Patents
Continual learning for topological geo-localisationCabrera-Ponce et al., 2023
- Document ID
- 17534525361172267847
- Author
- Cabrera-Ponce A
- Martin-Ortiz M
- Martinez-Carranza J
- Publication year
- Publication venue
- Journal of Intelligent & Fuzzy Systems
External Links
Snippet
Geo-localisation from a single aerial image for Uncrewed Aerial Vehicles (UAVs) is an alternative to other vision-based methods, such as visual Simultaneous Localisation and Mapping (SLAM), seeking robustness under GPS failure. Due to the success of deep …
Classifications
-
- 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
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- 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
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- 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
- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
-
- 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
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- 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
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Tian et al. | Search and rescue under the forest canopy using multiple UAVs | |
CN110956651B (en) | Terrain semantic perception method based on fusion of vision and vibrotactile sense | |
Lee et al. | Deep learning-based monocular obstacle avoidance for unmanned aerial vehicle navigation in tree plantations: Faster region-based convolutional neural network approach | |
US10983217B2 (en) | Method and system for semantic label generation using sparse 3D data | |
Wang et al. | Fast and accurate, convolutional neural network based approach for object detection from UAV | |
EP3920095A1 (en) | Image processing method and apparatus, moveable platform, unmanned aerial vehicle and storage medium | |
WO2020103108A1 (en) | Semantic generation method and device, drone and storage medium | |
WO2020103109A1 (en) | Map generation method and device, drone and storage medium | |
Vivaldini et al. | UAV route planning for active disease classification | |
Delmerico et al. | “on-the-spot training” for terrain classification in autonomous air-ground collaborative teams | |
Saleem et al. | Neural network-based recent research developments in SLAM for autonomous ground vehicles: A review | |
Mazzia et al. | Deepway: a deep learning waypoint estimator for global path generation | |
Iuzzolino et al. | Virtual-to-real-world transfer learning for robots on wilderness trails | |
Wang et al. | HypLiLoc: Towards effective LiDAR pose regression with hyperbolic fusion | |
Pham et al. | Gatenet: An efficient deep neural network architecture for gate perception using fish-eye camera in autonomous drone racing | |
Jiang et al. | Intelligent plant cultivation robot based on key marker algorithm using visual and laser sensors | |
Sinalkar et al. | Stereo vision-based path planning system for an autonomous harvester | |
Cabrera-Ponce et al. | Continual learning for topological geo-localisation | |
Chen et al. | Integrated air-ground vehicles for uav emergency landing based on graph convolution network | |
Abbas et al. | Autonomous canal following by a micro-aerial vehicle using deep cnn | |
Sleaman et al. | Indoor mobile robot navigation using deep convolutional neural network | |
Mazzia et al. | Deepway: a deep learning estimator for unmanned ground vehicle global path planning | |
Geng et al. | Deep learning-based cooperative trail following for multi-robot system | |
Luo et al. | An efficient visual servo tracker for herd monitoring by UAV | |
Wang et al. | Online drone-based moving target detection system in dense-obstructer environment |