Active and Interactive Mapping with Dynamic Gaussian Process Implicit Surfaces for Mobile Manipulators

TL;DR

This paper introduces an interactive mapping framework for mobile manipulators that uses dynamic Gaussian Process Implicit Surfaces to efficiently explore and manipulate objects in cluttered environments, balancing exploration and exploitation.

Contribution

It presents a novel dynamic GP-Implicit Surface method combined with an active next-best-view strategy that prioritizes boundary exploration for scene mapping and object picking.

Findings

Effective scene mapping with dynamic GP-Implicit Surfaces.

Balanced exploration and exploitation in object picking.

Validated through simulation and real-world experiments.

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 provides the next-best-view, balancing the need for picking object reachability with map information gain (IG). 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.