Sun et al., 2017 - Google Patents
A vision-based perception framework for outdoor navigation tasks applicable to legged robotsSun et al., 2017
- Document ID
- 4305200733797297310
- Author
- Sun J
- Meng Y
- Tan J
- Sun C
- Zhang J
- Ding N
- Qian H
- Zhang A
- Publication year
- Publication venue
- 2017 Chinese Automation Congress (CAC)
External Links
Snippet
A vision-based perception system for understanding the outdoor environment has been proposed. This system utilizes and applies the useful information from the binocular cameras of a legged robot for landform-terrain-ground environmental perception. The …
- 230000004438 eyesight 0 title abstract description 13
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0255—Control of position or course in two dimensions specially adapted to land vehicles using acoustic signals, e.g. ultra-sonic singals
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0246—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means unsing a video camera in combination with image processing means
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0268—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means
-
- 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
- G06K9/00664—Recognising scenes such as could be captured by a camera operated by a pedestrian or robot, including objects at substantially different ranges from the camera
- G06K9/00684—Categorising the entire scene, e.g. birthday party or wedding scene
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0287—Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/05—Geographic models
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Droeschel et al. | Continuous mapping and localization for autonomous navigation in rough terrain using a 3D laser scanner | |
Belter et al. | Employing natural terrain semantics in motion planning for a multi-legged robot | |
Waibel et al. | How rough is the path? terrain traversability estimation for local and global path planning | |
Lei et al. | Deep learning-based complete coverage path planning with re-joint and obstacle fusion paradigm | |
Hosseinpoor et al. | Traversability analysis by semantic terrain segmentation for mobile robots | |
Walas et al. | Terrain classification using laser range finder | |
Beycimen et al. | A comprehensive survey of unmanned ground vehicle terrain traversability for unstructured environments and sensor technology insights | |
Choi et al. | Improved CNN-based path planning for stairs climbing in autonomous UAV with LiDAR sensor | |
Bartoszyk et al. | Terrain-aware motion planning for a walking robot | |
Short et al. | Abio-inspiredalgorithminimage-based pathplanning and localization using visual features and maps | |
Prágr et al. | Incremental learning of traversability cost for aerial reconnaissance support to ground units | |
Li et al. | Seeing through the grass: Semantic pointcloud filter for support surface learning | |
Wang et al. | Aerial-Ground Collaborative Continuous Risk Mapping for Autonomous Driving of Unmanned Ground Vehicle in Off-Road Environments | |
Li et al. | Performance evaluation of 2D LiDAR SLAM algorithms in simulated orchard environments | |
Zhou et al. | An autonomous navigation approach for unmanned vehicle in outdoor unstructured terrain with dynamic and negative obstacles | |
Sun et al. | A vision-based perception framework for outdoor navigation tasks applicable to legged robots | |
Tiozzo Fasiolo et al. | Recent trends in mobile robotics for 3D mapping in agriculture | |
Mishra et al. | Perception engine using a multi-sensor head to enable high-level humanoid robot behaviors | |
Abbas et al. | Autonomous canal following by a micro-aerial vehicle using deep cnn | |
Muller et al. | Real-time adaptive off-road vehicle navigation and terrain classification | |
Wang | Autonomous mobile robot visual SLAM based on improved CNN method | |
Puck et al. | Modular, risk-aware mapping and fusion of environmental hazards | |
Sriram et al. | A hierarchical network for diverse trajectory proposals | |
Saucedo et al. | Memory Enabled Segmentation of Terrain for Traversability based Reactive Navigation | |
Wigness et al. | Reducing adaptation latency for multi-concept visual perception in outdoor environments |