We present a new 3D probabilistic occupancy map representation for robotics applications by relaxing the commonly-assumed constraint that space must be perfectly tessellated. We replace the regular structure of 3D grids with an unstructured collection of non-overlapping, equally-sized spheres, which we call “atoms”. Abandoning the grid structure allows a more accurate representation of space directly tangent to surfaces, which facilitates a number of applications such as high fidelity surface reconstruction and surface-guided path planning. Maps composed of atoms can distinguish between free, occupied, and unknown space, support computationally efficient insertions and collision queries, provide free space planning guarantees, and achieve state-of-the-art memory efficiency over large volumes. This is achieved while simultaneously reducing quantization effects in the vicinity of surfaces and defining a useful implicit surface representation.

An empirical study evaluated key human factors issues related to automation visibility and information quality, based on a refined definition of information automation. Next-generation air transportation system operational concepts will dramatically affect the types and amount of information available on flight decks. Information automation systems collect, process, and present information to support pilot tasks and awareness. The definition of flight deck information automation was refined to differentiate it from other types of automation. Pilots interacted with an example information automation system to investigate the premise that automation visibility will have an impact on the ability of pilots to detect problems resulting from poor information quality. Poor information quality appeared to be difficult for pilots to detect, even when presented with high automation visibility. Pilots tended to over-trust automation, so when reporting high workload and information was missing, they chose the top plan suggested by the automation even though it was not the best. Trust in automation was reduced by low information quality, but compensated for by increased automation visibility. Added information to help pilots understand information automation state and outputs, given a level of information quality, should be balanced against potential increases in pilot workload due to the time and attention needed to process the extra information.

We demonstrate distributed, online, and real-time cooperative localization and mapping between multiple robots operating throughout an unknown environment using indirect measurements. We present a novel Expectation Maximization (EM) based approach to efficiently identify inlier multi-robot loop closures by incorporating robot pose uncertainty, which significantly improves the trajectory accuracy over long-term navigation. An EM and hypothesis based method is used to determine a common reference frame. We detail a 2D laser scan correspondence method to form robust correspondences between laser scans shared amongst robots. The implementation is experimentally validated using teams of aerial vehicles, and analyzed to determine its accuracy, computational efficiency, scalability to many robots, and robustness to varying environments. We demonstrate through multiple experiments that our method can efficiently build maps of large indoor and outdoor environments in a distributed, online, and real-time setting.

We propose information-theoretic strategies for Occupancy Grid (OG) compression to enable high-speed exploration on computationally constrained mobile robots. We first formulate optimal lossy compression for OGs based on the Principle of Relevant Information. The solution to this formulation is a simple compression algorithm that is motivated by rate distortion theory. We then compress OGs to different resolutions and develop a second optimization based on the Information Bottleneck method that chooses a resolution simultaneously maximizing compression and minimizing loss of information from the robot's sensor measurements. On computationally constrained systems, the resulting reduction in computational complexity enables planning over longer predictive horizons, leading to higher-speed operation. Using these techniques to adaptively optimize OG resolution as the robot enters a new area causes it to autonomously slow down in obstacle-dense locations and speed up in open expanses. We simulate and experimentally evaluate mutual information-based exploration through cluttered indoor environments with exploration rates that adapt based on environment complexity, leading to an order-of-magnitude increase in the maximum rate of exploration in contrast to non-adaptive techniques given the same finite computational resources.

This paper presents a novel approach for multirobot pose graph localization and data association without requiring prior knowledge about the initial relative poses of the robots. Without a common reference frame, the robots can only share observations of interesting parts of the environment, and trying to match between observations from different robots will result in many outlier correspondences. Our approach is based on the following key observation: while each multi-robot correspondence can be used in conjunction with the local robot estimated trajectories, to calculate the transformation between the robot reference frames, only the inlier correspondences will be similar to each other. Using this concept, we develop an expectation-maximization (EM) approach to efficiently infer the robot initial relative poses and solve the multi-robot data association problem. Once this transformation between the robot reference frames is estimated with sufficient measure of confidence, we show that a similar EM formulation can be used to solve also the full multi-robot pose graph problem with unknown multi-robot data association. We evaluate the performance of the developed approach both in a statistical synthetic-environment study and in a real-data experiment, demonstrating its robustness to high percentage of outliers.

This paper briefly describes a system which provides suggested safe speeds for travel during inclement weather conditions. A user study was conducted to evaluate the interpretability of the interface and its effectiveness in influencing drivers' speed choices in inclement conditions. The results indicated that even naïve users were able to correctly interpret interface and adjusted their speed accordingly.