  Exploration tasks are essential to many emerging robotics applications,
ranging from search and rescue to space exploration. The planning problem for
exploration requires determining the best locations for future measurements
that will enhance the fidelity of the map, for example, by reducing its total
entropy. A widely-studied technique involves computing the Mutual Information
(MI) between the current map and future measurements, and utilizing this MI
metric to decide the locations for future measurements. However, computing MI
for reasonably-sized maps is slow and power hungry, which has been a bottleneck
towards fast and efficient robotic exploration. In this paper, we introduce a
new hardware accelerator architecture for MI computation that features a
low-latency, energy-efficient MI compute core and an optimized memory subsystem
that provides sufficient bandwidth to keep the cores fully utilized. The core
employs interleaving to counter the recursive algorithm, and workload balancing
and numerical approximations to reduce latency and energy consumption. We
demonstrate this optimized architecture with a Field-Programmable Gate Array
(FPGA) implementation, which can compute MI for all cells in an entire
201-by-201 occupancy grid ({\em e.g.}, representing a 20.1m-by-20.1m map at
0.1m resolution) in 1.55 ms while consuming 1.7 mJ of energy, thus finally
rendering MI computation for the whole map real time and at a fraction of the
energy cost of traditional compute platforms. For comparison, this particular
FPGA implementation running on the Xilinx Zynq-7000 platform is two orders of
magnitude faster and consumes three orders of magnitude less energy per MI map
compute, when compared to a baseline GPU implementation running on an NVIDIA
GeForce GTX 980 platform. The improvements are more pronounced when compared to
CPU implementations of equivalent algorithms.
