HTC Vive has been gaining attention as a cost-effective, off-the-shelf tracking system for collecting ground truth pose data. We assess this system's pose estimation through a series of controlled experiments where we show its precision to be in the millimeter magnitude and accuracy to range from millimeter to meter. We also show that Vive gives greater weight to inertial measurements in order to produce a smooth trajectory for virtual reality applications. Hence, the Vive's off the shelf algorithm is poorly suited for robotics applications such as measuring ground truth poses, where accuracy and repeatability are key. Therefore we introduce a new open-source tracking algorithm and calibration procedure for Vive which address these problems. We also show that our approach improves the pose estimation repeatability and accuracy by up to two orders of magnitude.

As the density of wireless, resource-constrained sensors grows, so does the need to choreograph their actions across both time and space. Recent advances in ultra-wideband RF communication have enabled accurate packet timestamping, which can be used to precisely synchronize time. Location may be further estimated by timing signal propagation, but this requires additional communication overhead to mitigate the effect of relative clock drift. This additional communication lowers overall channel efficiency and increases energy consumption. This article describes a novel approach to simultaneously localizing and time synchronizing networked mobile devices. An Extended Kalman Filter is used to estimate all devices’ positions and clock errors, and packet timestamps serve as measurements that constrain time and overall network geometry. By inspection of the uncertainty in our state estimate, we can adapt the number of messages sent in each communication round to balance accuracy with communication cost. This reduces communication overhead, which decreases channel congestion and power consumption compared to traditional time of arrival and time difference of arrival localization techniques. We demonstrate the performance and efficiency of our approach using a real network of custom RF devices and mobile quadrotors.

Having a shared and accurate sense of time is critical to distributed Cyber-Physical Systems (CPS) and the Internet of Things (IoT). Thanks to decades of research in clock technologies and synchronization protocols, it is now possible to measure and synchronize time across distributed systems with unprecedented accuracy. However, applications have not benefited to the same extent due to limitations of the system services that help manage time, and hardware-OS and OS-application interfaces through which timing information flows to the application. Due to the importance of time awareness in a broad range of emerging applications, running on commodity platforms and operating systems, it is imperative to rethink how time is handled across the system stack. We advocate the adoption of a holistic notion of Quality of Time (QoT) that captures metrics such as resolution, accuracy, and stability. Building on this notion we propose an architecture in which the local perception of time is a controllable operating system primitive with observable uncertainty, and where time synchronization balances applications' timing demands with system resources such as energy and bandwidth. Our architecture features an expressive application programming interface that is centered around the abstraction of a timeline - a virtual temporal coordinate frame that is defined by an application to provide its components with a shared sense of time, with a desired accuracy and resolution. The timeline abstraction enables developers to easily write applications whose activities are choreographed across time and space. Leveraging open source hardware and software components, we have implemented an initial Linux realization of the proposed timeline-driven QoT stack on a standard embedded computing platform. Results from its evaluation are also presented.

Motivated by the risks and costs associated with outdoor experiments, this paper presents a new multi-platform quadrotor simulator. The simulator implements a novel second-order dynamic model for a quadrotor, produced through evolutionary programming, and explained by domain knowledge. The model captures the effects of mechanics, aerodynamics, wind and rotational stabilization control on the flight platform. In addition, the simulator implements military-grade models for wind and turbulence, as well as noise models for satellite navigation, barometric altitude and orientation. The usefulness of the simulator is shown qualitatively by a comparing how coloured and white position noise affect the performance of offline, range-only SLAM. The simulator is intended to be used for planning experiments, or for stress-testing application performance over a wide range of operating conditions.

With sensor technology gaining maturity and becoming ubiquitous, we are experiencing an unprecedented wealth of sensor data. In most sensing applications, users receive sensor measurements, which are prone to error. As a result, they are often annotated with some measure of uncertainty, such as the distribution variance or a confidence interval, and will be hereafter referred to as probabilistic measurements. The question that we address in this paper is how to estimate the accuracy of these probabilistic measurements, that is, how far they lie from the ground truth of the measured attribute. Existing studies on estimating the accuracy of probabilistic measurements in real sensing applications are limited in three ways. First, they tend to be application-specific. Second, they typically employ learning techniques to estimate the parameters of sensor noise models, and ignore alternative approaches that rely on simple state estimation without learning. Third, they do not explore whether exploiting the dynamics of the monitored state can yield significant benefits in terms of accuracy estimation. In this paper, we address the above limitations as follows: We define the problem of accuracy estimation in a general way that applies to a wide spectrum of application scenarios. We then propose a taxonomy of accuracy estimation techniques, which include both state estimation and parameter learning. These techniques are further subdivided into static and dynamic, depending on whether they exploit knowledge of system dynamics. All different approaches in the taxonomy are then applied and compared with each other in the context of two real sensing applications. We discuss how they trade accuracy for computation cost, and how this tradeoff largely depends on the user's knowledge of the application scenario.

This paper addresses the problem of tracking a group of mobile sensors in an environment where there is intermittent or no access to a localization service, such as the Global Positioning System. Example applications include tracking personnel underground or animals under dense tree canopies. We assume that each sensor uses inertial, visual or mechanical odometry to measure its relative movement as a series of displacement vectors. Each displacement vector suffers a small quantity of error which compounds, causing the overall accuracy of the positional estimate to decrease with time. The primary contribution of this paper is a novel offline method of counteracting this error by exploiting opportunistic radio encounters between sensors. We fuse encounter information with the displacement vectors to build a graph that models sensor mobility. We show that two dimensional sensor tracking is equivalent to finding an embedding of this graph in the plane. Finally, using radio, inertial and ground truth trace data, we conduct simulations to observe how the number of anchors, transmission range and radio noise affect the performance of the proposed model. We compare these results to those from a competing model in the literature.

This paper is motivated by the real world problem of search and rescue by unmanned aerial vehicles (UAVs). We consider the problem of tracking a static target from a bird's-eye view camera mounted to the underside of a quadrotor UAV. We begin by proposing a target detection algorithm, which we then execute on a collection of video frames acquired from four different experiments. We show how the efficacy of the target detection algorithm changes as a function of altitude. We summarise this efficacy into a table which we denote the observation model. We then run the target detection algorithm on a sequence of video frames and use parameters from the observation model to update a recursive Bayesian estimator. The estimator keeps track of the probability that a target is currently in view of the camera, which we refer to more simply as target presence. Between each target detection event the UAV changes position and so the sensing region changes. Under certain assumptions regarding the movement of the UAV, the proportion of new information may be approximated to a value, which we then use to weight the prior in each iteration of the estimator. Through a series of experiments we show how the value of the prior for unseen regions, the altitude of the UAV and the camera sampling rate affect the accuracy of the estimator. Our results indicate that there is no single optimal sampling rate for all tested scenarios. We also show how the prior may be used as a mechanism for tuning the estimator according to whether a high false positive or high false negative probability is preferable.

Through their ability to rapidly acquire aerial imagery, Unmanned Aerial Vehicles (UAVs) have the potential to aid target search tasks. Many of the core algorithms which are used to plan search tasks use occupancy grid-based representations and are often based on two main assumptions. Firstly, the altitude of the UAV is constant. Secondly, the onboard sensors can measure the entire state of an entire grid cell. Although these assumptions are sufficient for fixed-wing, high speed UAVs, we do not believe that they are appropriate for small, lightweight, low speed and agile UAVs such as quadrotors. These platforms have the ability to change altitude and their low speed means that multiple measurements may easily overlap multiple cells for substantial periods of time. In this paper we extend a framework for probabilistic search based on decision making to incorporate multiple observations of grid cells and changes in UAV altitude. We account for observation areas that completely and partially cover multiple grid cells. We show the resultant impact on a number of simulation examples.

The distributed coordination function is one of three channel access control protocols specified by the IEEE 802.11 standard. In this paper we present a method of measuring DCF performance using a test bed built with off-the-shelf hardware. Performance is measured by normalized aggregate throughput as a function of the number of stations contending for channel access. We present measurements for both basic access and RTS/CTS access in fully-connected IEEE 802.11 g networks experiencing conditions of saturation. We compare our measurements to results from three analytic models and a simulator, all of which shared the same assumptions about the workload model and operation of DCF. For small networks the analytic models predict a much lower performance than shown through simulation and test bed experiments. As the network grows, so the measured performance deteriorates significantly faster than predicted by the analytic models. We attribute this to inaccuracies in the analytic model, imperfect channels and queuing. The simulation results fit the measured data with more accuracy, as the simulator makes fewer restrictive assumptions about DCF when compared to the analytic models. This is the first paper to provide a cross-comparison of test bed, simulation and analytic results for IEEE 802.11 g DCF performance.