Tripathi et al., 2014 - Google Patents
Occupancy grid mapping for mobile robot using sensor fusionTripathi et al., 2014
- Document ID
- 1864853949059783282
- Author
- Tripathi P
- Nagla K
- Singh H
- Mahajan S
- Publication year
- Publication venue
- 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)
External Links
Snippet
Sensor data fusion using more than one senor such as sonar sensors fusion reduces uncertainties generated from a single sensor. To learn the environment using more than one sensor information, an accurate sensor model as well as a reasonable sensor fusion …
- 230000004927 fusion 0 title abstract description 28
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
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting 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
- 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
- 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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/66—Radar-tracking systems; Analogous systems where the wavelength or the kind of wave is irrelevant
- G01S13/72—Radar-tracking systems; Analogous systems where the wavelength or the kind of wave is irrelevant for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar
- G01S13/723—Radar-tracking systems; Analogous systems where the wavelength or the kind of wave is irrelevant for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar by using numerical data
- G01S13/726—Multiple target tracking
-
- 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
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- 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
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Scherer et al. | River mapping from a flying robot: state estimation, river detection, and obstacle mapping | |
Bibby et al. | Simultaneous localisation and mapping in dynamic environments (SLAMIDE) with reversible data association | |
EP3734388A1 (en) | Method and apparatus for performing simultaneous localization and mapping | |
Li et al. | Neural network based FastSLAM for autonomous robots in unknown environments | |
KR101888295B1 (en) | Method for estimating reliability of distance type witch is estimated corresponding to measurement distance of laser range finder and localization of mobile robot using the same | |
Meizel et al. | Initial localization by set inversion | |
Tripathi et al. | Occupancy grid mapping for mobile robot using sensor fusion | |
Kurdej et al. | Controlling remanence in evidential grids using geodata for dynamic scene perception | |
Xu et al. | Dynamic vehicle pose estimation and tracking based on motion feedback for LiDARs | |
Clemens et al. | β-SLAM: Simultaneous localization and grid mapping with beta distributions | |
Dezert et al. | Environment perception using grid occupancy estimation with belief functions | |
Carlson et al. | Conflict metric as a measure of sensing quality | |
Sabatini et al. | Improving occupancy grid mapping via dithering for a mobile robot equipped with solid-state lidar sensors | |
Carlson et al. | Use of Dempster-Shafer conflict metric to detect interpretation inconsistency | |
Dekan et al. | Versatile approach to probabilistic modeling of Hokuyo UTM-30LX | |
Reineking et al. | Dimensions of uncertainty in evidential grid maps | |
Safin et al. | Modern Methods of Map Construction Using Optical Sensors Fusion | |
Lee et al. | Enhanced maximum likelihood grid map with reprocessing incorrect sonar measurements | |
Parsley et al. | Towards the exploitation of prior information in SLAM | |
Lee et al. | Development of advanced grid map building model based on sonar geometric reliability for indoor mobile robot localization | |
Martens et al. | Mobile robot sensor integration with fuzzy ARTMAP | |
Thallas et al. | Topological based scan Matching–Odometry posterior sampling in RBPF under kinematic model failures | |
Howard et al. | Sonar mapping for mobile robots | |
Vega-Brown | Predictive parameter estimation for Bayesian filtering | |
Kim et al. | Hierarchical sensor fusion for building an occupancy grid map using active sensor modules |