The paper investigates whether the methods chosen for representing uncertain geographic information aid or impair decision-making in the context of wildfire hazard. Through a series of three human subject experiments, utilizing 180 subjects and employing increasingly difficult tasks, this research evaluates the effect of five different visualizations and a text-based representation on decision-making under uncertainty. Our quantitative experiments focus specifically on the task of decision-making under uncertainty, rather than the task of reading levels of uncertainty from the map. To guard against the potential for generosity and risk seeking in decision-making under uncertainty, the experimental design uses performance-based incentives. The experiments showed that the choice of representation makes little difference to performance in cases where subjects are allowed the time and focus to consider their decisions. However, with the increasing difficulty of time pressure, subjects performed best using a spectral color hue-based representation, rather than more carefully designed cartographic representations. Text-based and simplified boundary encodings were among the worst performers. The results have implications for the performance of decision-making under uncertainty using static maps, especially in the stressful environments surrounding an emergency.

Space-based positioning, navigation, and timing (PNT) technologies, such as the global navigation satellite systems (GNSS) provide position, velocity, and timing information to an unlimited number of users around the world. In recent years, PNT information has become increasingly critical to the security, safety, and prosperity of the World's population, and is now widely recognized as an essential element of the global information infrastructure. Due to its vulnerabilities and line-of-sight requirements, GNSS alone is unable to provide PNT with the required levels of integrity, accuracy, continuity, and reliability. A multisensor navigation approach offers an effective augmentation in GNSS-challenged environments that holds a promise of delivering robust and resilient PNT. Traditionally, sensors such as inertial measurement units (IMUs), barometers, magnetometers, odometers, and digital compasses, have been used. However, recent trends have largely focused on image-based, terrain-based and collaborative navigation to recover the user location. This paper offers a review of the technological advances that have taken place in PNT over the last two decades, and discusses various hybridizations of multisensory systems, building upon the fundamental GNSS/IMU integration. The most important conclusion of this study is that in order to meet the challenging goals of delivering continuous, accurate and robust PNT to the ever-growing numbers of users, the hybridization of a suite of different PNT solutions is required.

As-is three-dimensional (3D) models of indoor environments are of paramount importance for a variety of applications such as navigation assistance and emergency response. Whereas manual reconstruction of 3D indoor models is a time-consuming task, methods for automatic or semiautomatic reconstruction can achieve a significant reduction of the time and labor required for indoor modeling. This paper proposes a new shape grammar approach for efficient generation of 3D models of indoor environments from point clouds. The shape grammar derives 3D models of complex environments by using a simple primitive and iterative application of grammar rules governed by a production procedure. The method can reconstruct both building elements and navigable spaces along with their topological relations. It produces 3D parametric models with high geometric accuracy and rich semantic content compliant with building information modeling (BIM) standards. Experiments on synthetic and real data sets show the ability of the method to generate highly accurate indoor models with computation times ranging from seconds to a few minutes. The usefulness of the output models in supporting indoor path planning at varying granularities is also demonstrated.

Cooperative positioning (CP) is an approach for positioning and/or positioning enhancement among a number of participants, which communicate and fuse their position-related information. Due to the shortcomings of Global Navigation Satellite Systems (GNSSs), modern CP approaches are considered for improving vehicular positioning where the GNSS cannot address the requirements of the specific applications such as collision avoidance or lane-level positioning. An inertial navigation system (INS) has not been considered for CP in the literature. The hybrid INS/GNSS methods used for positioning enhancement in standalone nodes cannot be classified as CP because the position-related data are not communicated between at least two independent entities. In this paper, we present a novel CP technique to improve INS-based positioning in vehicular networks. This cooperative inertial navigation (CIN) method can be used to enhance INS-based positioning in difficult GNSS environments, such as in very dense urban areas and tunnels. In the CIN method that is proposed, vehicles communicate their inertial measurement unit (IMU) and INS-based position data with oncoming vehicles traveling in the opposite direction. Each vehicle fuses the received data with those locally observed and the carrier frequency offset (CFO) of the received packets to improve the accuracy of its position estimates. The proposed method is analyzed using simulations and is also experimentally verified. The experimental results show up to 72% improvement in positioning over the standalone INS-based method.

The utility and adoption of indoor localization applications have been limited due to the complex nature of the physical environment combined with an increasing requirement for more robust localization performance. Existing solutions to this problem are either too expensive or too dependent on infrastructure such as Wi-Fi access points. To address this problem, we propose APFiLoc—a low cost, smartphone-based framework for indoor localization. The key idea behind this framework is to obtain landmarks within the environment and to use the augmented particle filter to fuse them with measurements from smartphone sensors and map information. A clustering method based on distance constraints is developed to detect organic landmarks in an unsupervised way, and the least square support vector machine is used to classify seed landmarks. A series of real-world experiments were conducted in complex environments including multiple floors and the results show APFiLoc can achieve 80% accuracy (phone in the hand) and around 70% accuracy (phone in the pocket) of the error less than 2 m error without the assistance of infrastructure like Wi-Fi access points.

Relative positioning among vehicles is a fundamental parameter for advanced applications of intelligent transportation systems such as collision avoidance and road safety. However, the level of positioning accuracy achievable using Global Navigation Satellite Systems does not meet the requirements of these applications. Cooperative positioning (CP) techniques can be used for improving the performance of absolute or relative positioning in a vehicular ad hoc network (VANET). The tight integration of Global Positioning System (GPS) data among communicating vehicles has already been introduced by the authors as a tight CP approach with specific advantages over differential GPS (DGPS) for relative positioning. In this paper, we propose an enhanced tight CP technique adding low-cost inertial navigation sensors and GPS Doppler shifts. Based on analytical and experimental results, the new method outperforms its predecessor and DGPS by 10% and 24%, respectively.