Multi-sensor fusion: a perspective

A survey of the state of the art in multisensor fusion is presented. Papers related to fusion have been surveyed and classified into six categories: scene segmentation, representation, 3-D shape, sensor modeling, autonomous robots, and object recognition. A number of fusion strategies have been employed to combine sensor outputs. These strategies range from simple set intersection, logical and operations, and heuristic production rules to more complex methods involving nonlinear least-squares fits and maximum-likelihood estimates. Sensor uncertainty has been modeled using Bayesian probabilities and support and plausibility involving the Dempster-Shafer formalism. >

Authors

• Jay K. Hackett
• Mubarak Shah

Keywords

• Sensor fusion
• Robot sensing systems
• Sensor phenomena and characterization
• Robotics and automation
• Manufacturing automation
• Layout
• Tactile sensors
• Sensor systems
• Computer science
• Target recognition
• Multi-sensor Fusion
• Maximum Likelihood
• Autonomic System
• Object Recognition
• Least-squares Fitting
• Nonlinear Least Squares
• Sensor Output
• Autonomous Navigation
• Multi-sensor System
• Scene Segmentation
• Automatic Target Recognition
• Bayesian Model
• Volume Of Data
• Visual Features
• Variance-covariance Matrix
• Environmental Dimensions
• Osteopontin
• Fusion Method
• Sensor Measurements
• Mahalanobis Distance
• Multiple Sensors
• Objects In The Scene
• Occupancy Grid
• Uncertainty Intervals
• Sensor Readings
• Fusion Strategy
• Tactile Sensor
• Optical Flow
• Direct Fusion
• Support For The Proposition