2016
Lifelong Information-Driven Exploration to Complete and Refine 4-D Spatio-Temporal Maps
RA-L 2016
This letter presents an exploration method that allows mobile robots to build and maintain spatio-temporal models of changing environments. The assumption of a perpetually changing world adds a temporal dimension to the exploration problem, making spatio-temporal exploration a never-ending, life-lon