In practical applications, many robots equipped with embedded devices have limited computing capabilities. These limitations often hinder the performance of existing dynamic SLAM algorithms, especially when faced with occlusions or processor constraints. Such challenges lead to subpar positioning accuracy and efficiency. This paper introduces a novel lightweight dynamic SLAM algorithm designed primarily to mitigate the interference caused by moving object occlusions. Our proposed approach combines a deep learning object detection algorithm with a Kalman filter. This combination offers prior information about dynamic objects for each SLAM algorithm frame. Leveraging geometric techniques like RANSAC and the epipolar constraint, our method filters out dynamic feature points, focuses on static feature points for pose determination, and enhances the SLAM algorithm’s robustness in dynamic environments. We conducted experimental validations on the TUM public dataset, which demonstrated that our approach elevates positioning accuracy by approximately 54% and boosts the running speed by 75.47% in dynamic scenes.

This paper presents the AffineMortality R package which performs parameter estimation, goodness-of-fit analysis, simulation, and projection of future mortality rates for a set of affine mortality models for use in pricing and reserving. The computational routines build on the univariate Kalman Filtering approach of Koopman and Durbin ((2000). Journal of Time Series Analysis, 21(3), 281–296.) along other numerical methods to enhance the robustness of the results. This paper provides a discussion of how the package works in order to effectively estimate and project survival curves, and describes the available functions. Illustration of the package for mortality analysis of the US male data set is provided.

A closed-form solution for zero-coupon bonds is obtained for a version of the discrete-time arbitrage-free Nelson-Siegel model. An estimation procedure relying on a Kalman filter is provided. The model is shown to produce adequate fit when applied to historical Canadian spot rate data and to improve distributional predictive performance over benchmarks. An adaptation of the mixed fund return model from Augustyniak et al. ((2021). ASTIN Bulletin: The Journal of the IAA, 51(1), 131–159.) is also provided to include the discrete-time arbitrage-free Nelson-Siegel model as one of its building blocks.

An accurate dynamic model of a robot is fundamentally important for a control system, while uncertainties residing in the model are inevitable in a physical robot system. The uncertainties can be categorized as internal disturbances and external disturbances in general. The former may include dynamic model errors and joint frictions, while the latter may include external payloads or human-exerted force to the robot. Disturbance observer is an important technique to estimate and compensate for the uncertainties of the dynamic model. Different types of disturbance observers have been developed to estimate the lumped uncertainties so far. In this paper, we conducted a brief survey on five typical types of observers from a perspective of practical implementation in a robot control system, including generalized momentum observer (GMO), joint velocity observer (JVOB), nonlinear disturbance observer (NDOB), disturbance Kalman filter (DKF), and extended state observer (ESO). First, we introduced the basics of each observer including equations and derivations. Two common types of disturbances are considered as two scenarios, that is, constant external disturbance and time-varying external disturbance. Then, the observers are separately implemented in each of the two simulated scenarios, and the disturbance tracking performance of each observer is presented while their performance in the same scenario has also been compared in the same figure. Finally, the main features and possible behaviors of each type of observer are summarized and discussed. This survey is devoted to helping readers learn the basic expressions of five typical observers and implement them in a robot control system.

A relatively novel approach of autonomous navigation employing platform dynamics as the primary process model raises new implementational challenges. These are related to: (i) potential numerical instabilities during longer flights; (ii) the quality of model self-calibration and its applicability to different flights; (iii) the establishment of a global estimation methodology when handling different initialisation flight phases; and (iv) the possibility of reducing computational load through model simplification. We propose a unified strategy for handling different flight phases with a combination of factorisation and a partial Schmidt–Kalman approach. We then investigate the stability of the in-air initialisation and the suitability of reusing pre-calibrated model parameters with their correlations. Without GNSS updates, we suggest setting a subset of the state vector as ‘considered’ states within the filter to remove their estimation from the remaining observations. We support all propositions with new empirical evidence: first in model-parameter self-calibration via optimal smoothing and second through applying our methods on three test flights with dissimilar durations and geometries. Our experiments demonstrate a significant improvement in autonomous navigation quality for twelve different scenarios.

For a common micro-satellite, orbiting in a circular sun-synchronous orbit (SSO) at an altitude between 500 and 600km, the satellite attitude during off-nadir imaging and staring-imaging operations can be up to ±45 degree on roll and pitch angles. During these off-nadir pointing for both multi-trip operation and staring imaging operations, the spacecraft body is commonly subject to high-rate motion. This posts challenges for a spacecraft attitude determination subsystem called Gyro Stellar Inertial Attitude Estimate (GS IAE), which employs gyros and star sensors to maintain the required attitude knowledge, since star trackers will severely degrade attitude estimation accuracies when the spacecraft is subject to high-rate motion. This paper analyses the star motion-induced errors for a typical star tracker, models the star motion-induced errors to assess the performance impact on the attitude estimation accuracy, and investigates the adaptive extended Kalman filter design in the GS IAE while evaluating its effectiveness.

In traditional satellite navigation receivers, the parameters of tracking loop such as loop bandwidth and integration time are usually set in the design of the receivers according to different scenarios. The signal tracking performance is limited in traditional receivers. In addition, when the tracking ability of weak signals is improved by extending the integration time, negative effect of residual frequency error becomes more and more serious with extension of the integration time. To solve these problems, this paper presents out research on receiver tracking algorithms and proposes an optimised tracking algorithm with inertial information. The receiver loop filter is designed based on Kalman filter, reducing the phase jitter caused by thermal noise in the weak signal environment and improving the signal tracking sensitivity. To confirm the feasibility of the proposed algorithm, simulation tests are conducted.

Safe interaction and inherent compliance with soft robots have motivated the evolution of soft rehabilitation robots. Among these, soft robotic gloves are known as an effective tool for stroke rehabilitation. This research proposed a pneumatically actuated soft robotic for index finger rehabilitation. The proposed system consists of a soft bending actuator and a sensing system equipped with four inertial measurement unit sensors to generate kinematic data of the index finger. The designed sensing system can estimate the range of motion (ROM) of the finger’s joints by combining angular velocity and acceleration values with the standard Kalman filter. The sensing system is evaluated regarding repeatability and reliability through static and dynamic experiments in the first step. The root mean square error attained in static and dynamic states are 2 $^\circ$ and 3 $^\circ$ , sequentially, representing an efficient function of the fusion algorithm. In the next step, experimental models have been developed to analyze and predict a soft actuator’s behavior in free and constrained states using the sensing system’s data. Thus, parametric system identification methods, artificial neural network—multilayer perceptron (ANN-MLP), and artificial neural network—radial basis function algorithms (ANN-RBF) have been compared to achieve an optimal model. The results reveal that ANN models, particularly RBF ones, can predict the actuator behavior with reasonable accuracy in the free and constrained state (<1 $^\circ$ ). Hence, the need for intricate analytical modeling and material characterization will be eliminated, and controlling the soft actuator will be more practical. Besides, it assesses the ROM and finger functionality.

This article shows how new time series models can be used to track the progress of an epidemic, forecast key variables and evaluate the effects of policies. The univariate framework of Harvey and Kattuman (2020, Harvard Data Science Review, Special Issue 1—COVID-19, https://hdsr.mitpress.mit.edu/pub/ozgjx0yn) is extended to model the relationship between two or more series and the role of common trends is discussed. Data on daily deaths from COVID-19 in Italy and the UK provides an example of leading indicators when there is a balanced growth. When growth is not balanced, the model can be extended by including a non-stationary component in one of the series. The viability of this model is investigated by examining the relationship between new cases and deaths in the Florida second wave of summer 2020. The balanced growth framework is then used as the basis for policy evaluation by showing how some variables can serve as control groups for a target variable. This approach is used to investigate the consequences of Sweden’s soft lockdown coronavirus policy in the spring of 2020.

Pedestrian dead reckoning (PDR) is widely used in handheld indoor positioning systems. However, low-cost inertial sensors built into smartphones provide poor-quality measurements, resulting in cumulative error which consists of heading estimation error caused by gyroscope and step length estimation error caused by an accelerometer. Learning more motion features through limited measurements is important to improve positioning accuracy. This paper proposes an improved PDR system using smartphone sensors. Using gyroscope, two motion patterns, walking straight or turning, can be recognised based on dynamic time warp (DTW) and thus improve heading estimation from an extended Kalman filter (EKF). Joint quasi-static field (JQSF) detection is used to avoid bad magnetic measurements due to magnetic disturbances in an indoor environment. In terms of periodicity of angular rate while walking, peak–valley angular velocity detection and zero-cross detection is combined to detect steps. A step-length estimation method based on deep belief network (DBN) is proposed. Experimental results demonstrate that the proposed PDR system can achieve more accurate indoor positioning.

A new class of time series models is used to track the progress of the COVID-19 epidemic in the UK in early 2021. Models are fitted to England and the regions, as well as to the UK as a whole. The growth rate of the daily number of cases and the instantaneous reproduction number are computed regularly and compared with those produced by SAGE. The results from figures published each day are compared with results based on figures by specimen date, which may be more accurate but are subject to substantial revisions. It is then shown how data from the two different sources can be combined in bivariate models.