Graph-based distributed cooperative navigation

This paper addresses the problem of distributed cooperative navigation. A new graph-based method is developed for on-demand calculation of the required correlation terms, considering a general multi-robot measurement model. These correlation terms are necessary for the consistent EKF-based data fusion when several statistically-dependent sources of information are used. The measurement model relates between the navigation information transmitted by any number of robots and the actual readings taken by the available onboard sensors. The transmitted information is not necessarily of the current time instant, but may actually belong to some time instant from the past. Experiment results and a theoretical example of the developed method are presented considering a three-view measurement, formulated upon receiving three images of the same scene, captured by different robots at different a priori unknown time instances.

Authors

• Vadim Indelman
• Pini Gurfil
• Ehud Rivlin
• Héctor Rotstein

Keywords

• Noise
• Navigation
• Correlation
• Particle measurements
• Atmospheric measurements
• Robot sensing systems
• Cooperative Navigation
• Measurement Model
• General Measures
• Time Instants
• Data Fusion
• Scene Images
• Navigation Information
• Theoretical Example
• Covariance Matrix
• Tertiary Education
• Measurement Noise
• Position Error
• Pair Of Nodes
• Process Noise
• Directed Acyclic Graph
• Noise Term
• General Scenario
• Statistical Independence
• Velocity Error
• Imagery Data
• East Direction
• North Direction
• Navigation Errors
• Relative Pose
• True Trajectory
• Navigation Data
• Descendant Nodes
• Robotic Group
• General Case
• Sensor Readings