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In this letter, we present an interactive probabilistic mapping framework fora 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 thescene by picking the chosen object, and then map these changes alongside. Theproposed framework uses a novel dynamic Gaussian Process (GP) Implicit Surfacemethod to incrementally build and update the scene map that reflectsenvironment 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 byexploiting the GP kernel derivative. This leads to an efficient strategy thataddresses the often conflicting requirement of unknown environment explorationand object picking exploitation given a limited execution horizon. Wedemonstrate the effectiveness of our framework with software simulation andreal-life experiments.

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