Mattamala et al., 2021 - Google Patents
Learning camera performance models for active multi-camera visual teach and repeatMattamala et al., 2021
View PDF- Document ID
- 3829128524597159090
- Author
- Mattamala M
- Ramezani M
- Camurri M
- Fallon M
- Publication year
- Publication venue
- 2021 IEEE International Conference on Robotics and Automation (ICRA)
External Links
Snippet
In dynamic and cramped industrial environments, achieving reliable Visual Teach and Repeat (VT&R) with a single-camera is challenging. In this work, we develop a robust method for non-synchronized multi-camera VT&R. Our contribution are expected Camera …
- 230000000007 visual effect 0 title abstract description 25
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
- G05D1/0287—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
- G05D1/0291—Fleet control
- G05D1/0295—Fleet control by at least one leading vehicle of the fleet
-
- 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
- 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
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D2201/00—Application
- G05D2201/02—Control of position of land vehicles
- G05D2201/0217—Anthropomorphic or bipedal robot
-
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Bonatti et al. | Towards a robust aerial cinematography platform: Localizing and tracking moving targets in unstructured environments | |
Smolyanskiy et al. | Toward low-flying autonomous MAV trail navigation using deep neural networks for environmental awareness | |
Price et al. | Deep neural network-based cooperative visual tracking through multiple micro aerial vehicles | |
Chen | Kalman filter for robot vision: a survey | |
Dey et al. | Vision and learning for deliberative monocular cluttered flight | |
Mattamala et al. | Learning camera performance models for active multi-camera visual teach and repeat | |
Chatterjee et al. | Mobile robot navigation | |
Tang et al. | Onboard detection-tracking-localization | |
Dequaire et al. | Off the beaten track: Predicting localisation performance in visual teach and repeat | |
Mahdavian et al. | Stpotr: Simultaneous human trajectory and pose prediction using a non-autoregressive transformer for robot follow-ahead | |
Yang et al. | Visual SLAM for autonomous MAVs with dual cameras | |
Augustine et al. | Landmark-tree map: a biologically inspired topological map for long-distance robot navigation | |
CN117152249A (en) | Multi-unmanned aerial vehicle collaborative mapping and perception method and system based on semantic consistency | |
Razali et al. | Visual simultaneous localization and mapping: a review | |
Fischer et al. | Stereo vision-based localization for hexapod walking robots operating in rough terrains | |
Jäger et al. | Efficient navigation based on the Landmark-Tree map and the Z∞ algorithm using an omnidirectional camera | |
Mishra et al. | Perception engine using a multi-sensor head to enable high-level humanoid robot behaviors | |
Abdulov et al. | Visual odometry approaches to autonomous navigation for multicopter model in virtual indoor environment | |
Yuan et al. | Visual steering of UAV in unknown environments | |
Zhura et al. | Neuroswarm: Multi-agent neural 3d scene reconstruction and segmentation with uav for optimal navigation of quadruped robot | |
Sriram et al. | A hierarchical network for diverse trajectory proposals | |
Leishman et al. | Robust Motion Estimation with RBG-D Cameras | |
Jung et al. | Visual cooperation based on LOS for self-organization of swarm robots | |
Dias et al. | Multi-robot cooperative stereo for outdoor scenarios | |
Ta et al. | Monocular parallel tracking and mapping with odometry fusion for mav navigation in feature-lacking environments |