3D point cloud registration is traditionally done by aligning to known information. This information can be extracted from semantically labeled and geo-registered 2D images, e.g. maps, satellite images, and labeled aerial photos. We propose an automated method to geo-register 3D point clouds to 2D maps by defining a normalized Hough similarity function and aligning planes (i.e., walls) in 3D point clouds to lines in 2D maps. The collective set of algorithms solves for seven degrees of freedom: three rotation parameters (including the up vector), a scale value, and three translation parameters. After transforming the 3D point cloud into a manageable 2D representation, we apply existing and novel scan-matching techniques to align both query and reference representations.

We propose a new approximate algorithm, LA- JIV (lookahead J-MDP information value), to solve oracular partially observable Markov decision problems (OPOMDPs), a special type of POMDP that rather than standard observations includes an "oracle" that can be consulted for full state information at a fixed cost. We previously introduced JIV (J-MDP information value) to solve OPOMDPs, an heuristic algorithm that utilizes the solution of the underlying MDP and weighs the value of consulting the oracle against the value of taking a state-modifying action. While efficient, JIV will rarely find the optimal solution. In this paper, we extend JIV to include lookahead, thereby permitting arbitrarily small deviation from the optimal policy's long-term expected reward at the cost of added computation time. The depth of the lookahead is a parameter that governs this tradeoff; by iteratively increasing this depth, we provide an anytime algorithm that yields an ever- improving solution. LA-JIV leverages the OPOMDP framework's unique characteristics to outperform general-purpose approximate POMDP solvers; in fact, we prove that LA-JIV is a poly-time approximation scheme (PTAS) with respect to the size of the state and observation spaces, thereby showing rigorously that OPOMDPs are "easier" than POMDPs. Finally, we substantiate our theoretical results via an empirical analysis of a benchmark OPOMDP instance.

We introduce the oracular partially observable Markov decision process (OPOMDP), a type of POMDP in which the world produces no observations; instead there is an "oracle," available in any state, that tells the agent its exact state for a fixed cost. The oracle may be a human or a highly accurate sensor. At each timestep the agent must choose whether to take a domain-level action or consult the oracle. This formulation comprises a factorization between information-gathering actions and domain-level actions, allowing us to characterize the value of information and to examine the problem of planning under uncertainty from a novel perspective. We propose an algorithm to capitalize on this factorization and the special structure of the OPOMDP, and we test the algorithm's performance on a new sample domain. On this new domain, we are able to solve a problem with hundreds of thousands of action-states and vastly outperform a previous state-of-the-art approximate technique