Evaluation of Kinematic Precise Point Positioning Convergence with an Incremental Graph Optimizer

Estimation techniques to precisely localize a kinematic platform with GNSS observables can be broadly partitioned into two categories: differential, or undifferenced. The differential techniques (e.g., real-time kinematic (RTK)) have several attractive properties, such as correlated error mitigation and fast convergence; however, to support a differential processing scheme, an infrastructure of reference stations within a proximity of the platform must be in place to construct observation corrections. This infrastructure requirement makes differential processing techniques infeasible in many locations. To mitigate the need for additional receivers within proximity of the platform, the precise point positioning (PPP) method utilizes accurate orbit and clock models to localize the platform. The autonomy of PPP from local reference stations make it an attractive processing scheme for several applications; however, a current disadvantage of PPP is the slow positioning convergence when compared to differential techniques. In this paper, we evaluate the convergence properties of PPP with an incremental graph optimization scheme (Incremental Smoothing and Mapping (iSAM2)), which allows for real-time filtering and smoothing. The characterization is first conducted through a Monte Carlo analysis within a simulation environment, which allows for the variations of parameters, such as atmospheric conditions, satellite geometry, and intensity of multipath. Then, an example collected data set is utilized to validate the trends presented in the simulation study.Comment: 8 page

Galápagos sea lion behavior differences in relation to human exposure

Our study examined the behavioral differences of the Galápagos sea lion (Zalophus wollebaeki) in relation to human presence. Our main goal was to determine whether sea lions would be more aggressive as a result of high frequencies of human exposure. We hypothesized that sea lions would behave differently in relation to varying rates of human exposure and we predicted that there would be more aggressive and interactive behaviors on beaches with higher frequencies of human exposure (as the humans may disturb the normal behavioral patterns of the sea lions). Data was collected daily at low tide in two-hour intervals. Our study took place during July 2014 on Isla San Cristóbal on three beaches near Puerto Baquerizo Moreno, Galápagos Islands, Ecuador. We recorded the number of people and sea lions on each beach during each data collection, as well as any observed behavioral characteristics of sea lions. We categorized behavioral characteristics of sea lions as aggressive, interactive but non-aggressive, and non-interactive both on terrestrial and aquatic environments. In addition, we accounted for the frequency of interactions in relation to the size of the beach in which data was collected. Results from a Chi-squared goodness of fit test showed that there was a significant difference in the sea lions’ behavior in relation to human exposure (p \u3c 0.0001). Further analysis showed that sea lions tend to be more aggressive in response to higher frequencies of human exposure (p \u3c 0.0001). Previous studies have shown that high rates of human exposure in sea lion habitats can result in a decrease of sea lion populations (French et al., 2011). With regards to these results, there should be a consideration for how human exposure can affect the behavior of sea lions. Tourism in the Galápagos Islands remains prevalent, which can potentially disrupt the natural behavior of protected species if humans disrupt the animals’ natural behavior

Flight-Test Evaluation of Kinematic Precise Point Positioning of Small UAVs

An experimental analysis of Global Positioning System (GPS) flight data collected onboard a Small Unmanned Aerial Vehicle (SUAV) is conducted in order to demonstrate that postprocessed kinematic Precise Point Positioning (PPP) solutions with precisions approximately 6 cm 3D Residual Sum of Squares (RSOS) can be obtained on SUAVs that have short duration flights with limited observational periods (i.e., only ∼≤5 minutes of data). This is a significant result for the UAV flight testing community because an important and relevant benefit of the PPP technique over traditional Differential GPS (DGPS) techniques, such as Real-Time Kinematic (RTK), is that there is no requirement for maintaining a short baseline separation to a differential GNSS reference station. Because SUAVs are an attractive platform for applications such as aerial surveying, precision agriculture, and remote sensing, this paper offers an experimental evaluation of kinematic PPP estimation strategies using SUAV platform data. In particular, an analysis is presented in which the position solutions that are obtained from postprocessing recorded UAV flight data with various PPP software and strategies are compared to solutions that were obtained using traditional double-differenced ambiguity fixed carrier-phase Differential GPS (CP-DGPS). This offers valuable insight to assist designers of SUAV navigation systems whose applications require precise positionin

Reducing Youth Risk Behaviors Through Interactive Theater Intervention

The reduction of risk behaviors in secondary schools is a key concern for parents, teachers, and school administrators. School is one of the primary contexts of socialization for young people; thus, the investment in school-based programs to reduce risk behaviors is essential. In this study, we report on youth who participated in an intervention designed to improve decision-making skills based on positive youth development approaches. We examine changes in decision-making skills before and after involvement in the Teen Interactive Theater Education (TITE) program and retrospective self-assessment of change in knowledge, abilities, and beliefs as a result of participating in TITE (n = 127). Youth that reported increases in knowledge, abilities, and beliefs due to the intervention (n = 89) were more likely to think about the consequences of their decisions and list options before making a decision compared to their counterparts that reported less overall learning (n = 38). Implications for intervention research and stakeholders are discussed

Disentangling within-person changes and individual differences among fundamental need satisfaction, attainment of acquisitive desires, and psychological health

We explored within-person and individual difference associations among basic psychological need satisfaction (autonomy, competence, and relatedness), attainment of acquisitive desires (wealth and popularity) and indicators of well- and ill-being. Participants were 198 undergraduates (51% male) who completed an inventory multiple times over a university semester. Analyses revealed that increased satisfaction of all the needs and desires beyond participants’ normal levels, with the exception of relatedness, were associated with greater psychological welfare. Nonetheless, individual differences in well-being were only predicted by psychological need satisfaction, and not by the attainment of acquisitive desires. Hence, the realization of acquisitive desires may elicit within-person increases in psychological welfare; however, satisfying innate needs may be a better bet for long term psychological health

Enabling Robust State Estimation through Measurement Error Covariance Adaptation

Accurate platform localization is an integral component of most robotic systems. As these robotic systems become more ubiquitous, it is necessary to develop robust state estimation algorithms that are able to withstand novel and non-cooperative environments. When dealing with novel and non-cooperative environments, little is known a priori about the measurement error uncertainty, thus, there is a requirement that the uncertainty models of the localization algorithm be adaptive. Within this paper, we propose the batch covariance estimation technique, which enables robust state estimation through the iterative adaptation of the measurement uncertainty model. The adaptation of the measurement uncertainty model is granted through non-parametric clustering of the residuals, which enables the characterization of the measurement uncertainty via a Gaussian mixture model. The provided Gaussian mixture model can be utilized within any non-linear least squares optimization algorithm by approximately characterizing each observation with the sufficient statistics of the assigned cluster (i.e., each observation's uncertainty model is updated based upon the assignment provided by the non-parametric clustering algorithm). The proposed algorithm is verified on several GNSS collected data sets, where it is shown that the proposed technique exhibits some advantages when compared to other robust estimation techniques when confronted with degraded data quality.Comment: 14 pages, 13 figures, Submitted to IEEE Transactions on Aerospace And Electronic System

Uncertainty Model Estimation in an Augmented Data Space for Robust State Estimation

The requirement to generate robust robotic platforms is a critical enabling step to allow such platforms to permeate safety-critical applications (i.e., the localization of autonomous platforms in urban environments). One of the primary components of such a robotic platform is the state estimation engine, which enables the platform to reason about itself and the environment based upon sensor readings. When such sensor readings are degraded traditional state estimation approaches are known to breakdown. To overcome this issue, several robust state estimation frameworks have been proposed. One such method is the batch covariance estimation (BCE) framework. The BCE approach enables robust state estimation by iteratively updating the measurement error uncertainty model through the fitting of a Gaussian mixture model (GMM) to the measurement residuals. This paper extends upon the BCE approach by arguing that the uncertainty estimation process should be augmented to include metadata (e.g., the signal strength of the associated GNSS observation). The modification of the uncertainty estimation process to an augmented data space is significant because it increases the likelihood of a unique partitioning in the measurement residual domain and thus provides the ability to more accurately characterize the measurement uncertainty model. The proposed batch covariance estimation over an augmented data-space (BCE-AD) is experimentally validated on collected data where it is shown that a significant increase in state estimation accuracy can be granted compared to previously proposed robust estimation techniques.Comment: 6 pages, 5 figures, Correspondence submitted to the IEEE Transactions on Aerospace and Electronic System

Robust Incremental State Estimation through Covariance Adaptation

Recent advances in the fields of robotics and automation have spurred significant interest in robust state estimation. To enable robust state estimation, several methodologies have been proposed. One such technique, which has shown promising performance, is the concept of iteratively estimating a Gaussian Mixture Model (GMM), based upon the state estimation residuals, to characterize the measurement uncertainty model. Through this iterative process, the measurement uncertainty model is more accurately characterized, which enables robust state estimation through the appropriate de-weighting of erroneous observations. This approach, however, has traditionally required a batch estimation framework to enable the estimation of the measurement uncertainty model, which is not advantageous to robotic applications. In this paper, we propose an efficient, incremental extension to the measurement uncertainty model estimation paradigm. The incremental covariance estimation (ICE) approach, as detailed within this paper, is evaluated on several collected data sets, where it is shown to provide a significant increase in localization accuracy when compared to other state-of-the-art robust, incremental estimation algorithms.Comment: 8 pages, 4 figures, 2 tables, submitted to IEEE Robotics and Automation Letter