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 computationally and energywise expensive, often prohibitive for smaller robots and drones, which has been the bottleneck towards fast and efficient robotic exploration. As a workaround, MI is often only computed for a sparse set of points or computed at a rate slower than the update of the map, techniques which fail to provide theoretical guarantees on the efficiency of exploration.In this thesis, we introduce a new hardware accelerator architecture for MI computation that features a high-efficiency 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 on a Field-Programmable Gate Array (FPGA) implementation, which can compute MI for all cells in an entire 201-by-201 grid (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 times 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.

Computing consumes a significant portion of energy in many robotics applications, especially the ones involving energy-constrained robots. In addition, memory access accounts for a significant portion of the computing energy. For mapping a 3D environment, prior approaches reduce the map size while incurring a large memory overhead used for storing sensor measurements and temporary variables during computation. In this work, we present a memory-efficient algorithm, named Single-Pass Gaussian Fitting (SPGF), that accurately constructs a compact Gaussian Mixture Model (GMM) which approximates measurements from a depthmap generated from a depth camera. By incrementally constructing the GMM one pixel at a time in a single pass through the depthmap, SPGF achieves higher throughput and orders-of-magnitude lower memory overhead than prior multi-pass approaches. By processing the depthmap row-by-row, SPGF exploits intrinsic properties of the camera to efficiently and accurately infer surface geometries, which leads to higher precision than prior approaches while maintaining the same compactness of the GMM. Using a low-power ARM Cortex-A57 CPU on the NVIDIA Jetson TX2 platform, SPGF operates at 32fps, requires 43KB of memory overhead, and consumes only 0.11J per frame (depthmap). Thus, SPGF enables real-time mapping of large 3D environments on energy-constrained robots.

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Exploration problems are fundamental to robotics, arising in various domains, ranging from search and rescue to space exploration. Many effective exploration algorithms rely on the computation of mutual information between the current map and potential future measurements in order to make planning decisions. Unfortunately, computing mutual information metrics is computationally challenging. In fact, a large fraction of the current literature focuses on approximation techniques to devise computationally-efficient algorithms. In this paper, we propose a novel computing hardware architecture to efficiently compute Shannon mutual information. The proposed architecture consists of multiple mutual information computation cores, each evaluating the mutual information between a single sensor beam and the occupancy grid map. The key challenge is to ensure that each core is supplied with data when requested, so that all cores are maximally utilized. Our key contribution consists of a novel memory architecture and data delivery method that ensures effective utilization of all mutual information computation cores. This architecture was optimized for 16 mutual information computation cores, and was implemented on an FPGA. We show that it computes the mutual information metric for an entire map of 20m × 20m at 0.1m resolution in near real time, at 2 frames per second, which is approximately two orders of magnitude faster, while consuming an order of magnitude less power, when compared to an equivalent implementation on a Xeon CPU.