  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.
