Georeferencing of Laser Scanner-Based Kinematic Multi-Sensor Systems in the Context of Iterated Extended Kalman Filters Using Geometrical Constraints.

Georeferencing is an indispensable necessity regarding operating with kinematic multi-sensor systems (MSS) in various indoor and outdoor areas. Information from object space combined with various types of prior information (e.g., geometrical constraints) are beneficial especially in challenging environments where common solutions for pose estimation (e.g., global navigation satellite system or external tracking by a total station) are inapplicable, unreliable or inaccurate. Consequently, an iterated extended Kalman filter is used and a general georeferencing approach by means of recursive state estimation is introduced. This approach is open to several types of observation inputs and can deal with (non)linear systems and measurement models. The capability of using both explicit and implicit formulations of the relation between states and observations, and the consideration of (non)linear equality and inequality state constraints is a special feature. The framework presented is evaluated by an indoor kinematic MSS based on a terrestrial laser scanner. The focus here is on the impact of several different combinations of applied state constraints and the dependencies of two classes of inertial measurement units (IMU). The results presented are based on real measurement data combined with simulated IMU measurements.

Integrity and Collaboration in Dynamic Sensor Networks.

Global Navigation Satellite Systems (GNSS) deliver absolute position and velocity, as well as time information (P, V, T). However, in urban areas, the GNSS navigation performance is restricted due to signal obstructions and multipath. This is especially true for applications dealing with highly automatic or even autonomous driving. Subsequently, multi-sensor platforms including laser scanners and cameras, as well as map data are used to enhance the navigation performance, namely in accuracy, integrity, continuity and availability. Although well-established procedures for integrity monitoring exist for aircraft navigation, for sensors and fusion algorithms used in automotive navigation, these concepts are still lacking. The research training group i.c.sens, integrity and collaboration in dynamic sensor networks, aims to fill this gap and to contribute to relevant topics. This includes the definition of alternative integrity concepts for space and time based on set theory and interval mathematics, establishing new types of maps that report on the trustworthiness of the represented information, as well as taking advantage of collaboration by improved filters incorporating person and object tracking. In this paper, we describe our approach and summarize the preliminary results.

Computationally Efficient Implementation of a Novel Algorithm for the General Unified Threshold Model of Survival (GUTS).

The General Unified Threshold model of Survival (GUTS) provides a consistent mathematical framework for survival analysis. However, the calibration of GUTS models is computationally challenging. We present a novel algorithm and its fast implementation in our R package, GUTS, that help to overcome these challenges. We show a step-by-step application example consisting of model calibration and uncertainty estimation as well as making probabilistic predictions and validating the model with new data. Using self-defined wrapper functions, we show how to produce informative text printouts and plots without effort, for the inexperienced as well as the advanced user. The complete ready-to-run script is available as supplemental material. We expect that our software facilitates novel re-analysis of existing survival data as well as asking new research questions in a wide range of sciences. In particular the ability to quickly quantify stressor thresholds in conjunction with dynamic compensating processes, and their uncertainty, is an improvement that complements current survival analysis methods.

Modelling survival: exposure pattern, species sensitivity and uncertainty.

The General Unified Threshold model for Survival (GUTS) integrates previously published toxicokinetic-toxicodynamic models and estimates survival with explicitly defined assumptions. Importantly, GUTS accounts for time-variable exposure to the stressor. We performed three studies to test the ability of GUTS to predict survival of aquatic organisms across different pesticide exposure patterns, time scales and species. Firstly, using synthetic data, we identified experimental data requirements which allow for the estimation of all parameters of the GUTS proper model. Secondly, we assessed how well GUTS, calibrated with short-term survival data of Gammarus pulex exposed to four pesticides, can forecast effects of longer-term pulsed exposures. Thirdly, we tested the ability of GUTS to estimate 14-day median effect concentrations of malathion for a range of species and use these estimates to build species sensitivity distributions for different exposure patterns. We find that GUTS adequately predicts survival across exposure patterns that vary over time. When toxicity is assessed for time-variable concentrations species may differ in their responses depending on the exposure profile. This can result in different species sensitivity rankings and safe levels. The interplay of exposure pattern and species sensitivity deserves systematic investigation in order to better understand how organisms respond to stress, including humans.