The datum particle filter: Localization for objects with coupled geometric datums

In this paper, we propose a touch-based localization approach for a potentially large and complex object with multiple internal degrees of freedom. Should a task only require a partial localization of the object, our method selects the appropriate information gathering actions to register the desired features. We use probabilistic methods to reason over the distribution of the estimated object poses in the 6-DOF configuration space. We introduce the datum-based particle filter to handle intrinsic tolerances between each of the sections of the object. We describe two alternative methods for the particle filter system: one using the full joint belief and the other reasonably simplifying the belief to achieve a better ability to scale. We present simulation results for both proposed methods to show the advantages of our approaches.

Authors

• Shiyuan Chen
• Brad Saund
• Reid G. Simmons

Keywords

• Atmospheric measurements
• Particle measurements
• Sensors
• Solid modeling
• Uncertainty
• Robots
• Particle Filter
• Configuration Space
• Objective Of This Section
• Object Pose
• Internal Degrees Of Freedom
• Measurement Values
• Rigid Body
• Kalman Filter
• Information Gain
• Continuous Distribution
• Part Of Section
• Formation Of Contacts
• Distance Map
• Tactile Sensor
• Single Section
• Extended Kalman Filter
• Conditional Entropy
• CAD Model
• Rejection Sampling
• Belief State
• Unscented Kalman Filter
• Belief Updating
• Contact Sensors
• Bayesian Filtering
• World Frame
• Object Geometry
• Assembly Process
• Process Model
• Geometric Relationship
• Dimension Of The State Space