  Planning under partial obervability is essential for autonomous robots. A
principled way to address such planning problems is the Partially Observable
Markov Decision Process (POMDP). Although solving POMDPs is computationally
intractable, substantial advancements have been achieved in developing
approximate POMDP solvers in the past two decades. However, computing robust
solutions for problems with continuous observation spaces remains challenging.
Most on-line solvers rely on discretising the observation space or artificially
limiting the number of observations that are considered during planning to
compute tractable policies. In this paper we propose a new on-line POMDP
solver, called Lazy Belief Extraction for Continuous POMDPs (LABECOP), that
combines methods from Monte-Carlo-Tree-Search and particle filtering to
construct a policy reprentation which doesn't require discretised observation
spaces and avoids limiting the number of observations considered during
planning. Experiments on three different problems involving continuous
observation spaces indicate that LABECOP performs similar or better than
state-of-the-art POMDP solvers.
