Title
Active And Interactive Mapping With Dynamic Gaussian Process Implicit Surfaces For Mobile Manipulators
Abstract
In this letter, we present an interactive probabilistic mapping framework for a mobile manipulator picking objects from a pile. The aim is to map the scene, actively decide where to go next and which object to pick, make changes to the scene by picking the chosen object, and then map these changes alongside. The proposed framework uses a novel dynamic Gaussian Process (GP) Implicit Surface method to incrementally build and update the scene map that reflects environment changes. Actively the framework computes the next-best-view, balancing the terms of object reachability for picking and map information gain (IG) for fidelity and coverage. To enforce a priority of visiting boundary segments over unknown regions, the IG formulation includes an uncertainty gradient-based frontier score by exploiting the GP kernel derivative. This leads to an efficient strategy that addresses the often conflicting requirement of unknown environment exploration and object picking exploitation given a limited execution horizon. We demonstrate the effectiveness of our framework with software simulation and real-life experiments.
Year
DOI
Venue
2021
10.1109/LRA.2021.3061324
IEEE ROBOTICS AND AUTOMATION LETTERS
Keywords
DocType
Volume
Automatic building construction, exploration, gaussian process implicit surfaces, mapping, mobile manipulator
Journal
6
Issue
ISSN
Citations 
2
2377-3766
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Liyang Liu122.13
Simon Fryc200.34
Lan Wu301.01
Thanh Vu400.34
Gavin Paul5387.68
Teresa A. Vidal-Calleja67315.59