Active and Interactive Mapping with Dynamic Gaussian Process Implicit Surfaces for Mobile Manipulators
This addresses the challenge of efficient scene mapping and object picking for mobile manipulators in dynamic environments, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of enabling a mobile manipulator to map and pick objects from a pile by developing an interactive probabilistic mapping framework that uses dynamic Gaussian Process Implicit Surfaces to update the scene map and actively decide next actions, demonstrating effectiveness in simulation and real-life experiments.
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.