With many applications requiring individuals to share spaces with autonomous systems, not only do accurate positioning solutions become crucial, but also understanding of the uncertainty associated with these systems. For applications like public transportation or logistics, position tracking algorithms need to be evaluated on accuracy and consistency. The selection of the appropriate algorithms heavily relies on the specific requirements of the applications, thus demanding novel algorithms for these new use cases.This paper introduces a novel error state Kalman filter with implicit measurement equations, presenting a solution for new applications. The filter facilitates the fusion of inertial measurement unit (IMU) sensor data with other sensors using arbitrary measurement models. To showcase its effectiveness, the filter is demonstrated by fusing simulated IMU and LiDAR observations for position tracking (complete code on Github). Specifically, the LiDAR points are employed in the update step by minimizing the distances to known planes. Furthermore, the performance of the filter is enhanced by motion compensation through pose interpolation, utilizing the available timestamps. The results are thoroughly discussed in terms of accuracy and consistency, revealing significant improvements by employing pose interpolation. However, the consistency analysis indicates slightly pessimistic results, suggesting the need for further optimizations.

Multi-Sensor-System (MSS) georeferencing is a challenging task in engineering that should be dealt with in the most accurate way possible. The easiest and most straightforward way for this purpose is to rely on Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) data. However, at indoor environments or crowded inner-city areas, such data are not accurate to be entirely relied on. Therefore, appropriate filtering algorithms are required to compensate for possible errors and to improve the accuracy of the results. Sometimes it is also possible to increase the functionality of a filtering technique by engaging additional complementary information that can directly influence the outputs. Such information could be, e.g. geometrical features of the environment in which the MSS runs through. The current paper deals with MSS georeferencing by means of a Dual State Iterated Extended Kalman Filter (DSIEKF) that is based on an efficient combination of the Iterated Extended Kalman Filter (IEKF) with implicit measurement equations technique and nonlinear geometrical constraints. Final results of such an algorithm are shown to be satisfactory not only from the accuracy point of view but also the computation time.

Accurate, reliable and complete georeferencing with kinematic multi-sensor systems (MSS) is very demanding if common types of observations (e.g. usually GNSS) are imprecise or completely absence. The main reasons for this are challenging areas of indoor applications or inner-city areas with shadowing and multipath effects. However, those complex and tough environments are rather the rule than the exception. Consequently, we are developing an information-based georeferencing approach which can still estimate precise and accurate pose parameters when other current methods may fail. We modified an iterated extended Kalman filter (IEKF) approach which can deal with implicit measurement equations and introduced nonlinear equality constraints for the state parameters to integrate additional information. Hence, we can make use of geometric circumstances in the direct environment of the MSS and provide a more precise and reliable georeferencing.

This paper gives an overview on several current indoor georeferencing methods for kinematic multi sensor systems (MSS) and compares them to each other. Key issue is the application of these methods in complex indoor environments like office spaces with many separate rooms and contorted structures. Furthermore, a new universal knowledge-based approach for accurate indoor georeferencing will be introduced theoretically. This novel method will contain additional prior knowledge about the object space and could be applied in almost any indoor environment. The achieved georeferencing solution has the potential to be much more robust, efficient, accurate, complete and reliable than any other existing georeferencing method in mentioned complex structures.

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.