Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions

Inertial sensing is used in many applications and platforms, ranging from day-to-day devices such as smartphones to very complex ones such as autonomous vehicles. In recent years, the development of machine learning and deep learning techniques has increased significantly in the field of inertial sensing and sensor fusion. This is due to the development of efficient computing hardware and the accessibility of publicly available sensor data. These data-driven approaches mainly aim to empower model-based inertial sensing algorithms. To encourage further research in integrating deep learning with inertial navigation and fusion and to leverage their capabilities, this paper provides an in-depth review of deep learning methods for inertial sensing and sensor fusion. We discuss learning methods for calibration and denoising as well as approaches for improving pure inertial navigation and sensor fusion. The latter is done by learning some of the fusion filter parameters. The reviewed approaches are classified by the environment in which the vehicles operate: land, air, and sea. In addition, we analyze trends and future directions in deep learning-based navigation and provide statistical data on commonly used approaches

A-KIT: Adaptive Kalman-Informed Transformer

The extended Kalman filter (EKF) is a widely adopted method for sensor fusion in navigation applications. A crucial aspect of the EKF is the online determination of the process noise covariance matrix reflecting the model uncertainty. While common EKF implementation assumes a constant process noise, in real-world scenarios, the process noise varies, leading to inaccuracies in the estimated state and potentially causing the filter to diverge. To cope with such situations, model-based adaptive EKF methods were proposed and demonstrated performance improvements, highlighting the need for a robust adaptive approach. In this paper, we derive and introduce A-KIT, an adaptive Kalman-informed transformer to learn the varying process noise covariance online. The A-KIT framework is applicable to any type of sensor fusion. Here, we present our approach to nonlinear sensor fusion based on an inertial navigation system and Doppler velocity log. By employing real recorded data from an autonomous underwater vehicle, we show that A-KIT outperforms the conventional EKF by more than 49.5% and model-based adaptive EKF by an average of 35.4% in terms of position accuracy

It pays to be nice, but not really nice: Asymmetric evaluations of prosociality across seven cultures.

Cultures differ in many important ways, but one trait appears to be universally valued: prosociality. For one’s reputation, around the world, it pays to be nice to others. However, recent research with American participants finds that evaluations of prosocial actions are asymmetric—relatively selfish actions are evaluated according to the magnitude of selfishness but evaluations of relatively generous actions are less sensitive to magnitude. Extremely generous actions are judged roughly as positively as modestly generous actions, but extremely selfish actions are judged much more negatively than modestly selfish actions (Klein & Epley, 2014). Here we test whether this asymmetry in evaluations of prosociality is culture-specific. Across 7 countries, 1,240 participants evaluated actors giving various amounts of money to a stranger. Along with relatively minor cross-cultural differences in evaluations of generous actions, we find cross-cultural similarities in the asymmetry in evaluations of prosociality. We discuss implications for how reputational inferences can enable the cooperation necessary for successful societies

Measuring Intersectional Biases in Historical Documents

Data-driven analyses of biases in historical texts can help illuminate the origin and development of biases prevailing in modern society. However, digitised historical documents pose a challenge for NLP practitioners as these corpora suffer from errors introduced by optical character recognition (OCR) and are written in an archaic language. In this paper, we investigate the continuities and transformations of bias in historical newspapers published in the Caribbean during the colonial era (18th to 19th centuries). Our analyses are performed along the axes of gender, race, and their intersection. We examine these biases by conducting a temporal study in which we measure the development of lexical associations using distributional semantics models and word embeddings. Further, we evaluate the effectiveness of techniques designed to process OCR-generated data and assess their stability when trained on and applied to the noisy historical newspapers. We find that there is a trade-off between the stability of the word embeddings and their compatibility with the historical dataset. We provide evidence that gender and racial biases are interdependent, and their intersection triggers distinct effects. These findings align with the theory of intersectionality, which stresses that biases affecting people with multiple marginalised identities compound to more than the sum of their constituents

Impact of Aeolian Dry Deposition of Reactive Iron Minerals on Sulfur Cycling in Sediments of the Gulf of Aqaba

The Gulf of Aqaba is an oligotrophic marine system with oxygen-rich water column and organic carbon-poor sediments (≤0.6% at sites that are not influenced by anthropogenic impact). Aeolian dust deposition from the Arabian, Sinai, and Sahara Deserts is an important source of sediment, especially at the deep-water sites of the Gulf, which are less affected by sediment transport from the Arava Desert during seasonal flash floods. Microbial sulfate reduction in sediments is inferred from the presence of pyrite (although at relatively low concentrations), the presence of sulfide oxidation intermediates, and by the sulfur isotopic composition of sulfate and solid-phase sulfides. Saharan dust is characterized by high amounts of iron minerals such as hematite and goethite. We demonstrated, that the resulting high sedimentary content of reactive iron(III) (hydr)oxides, originating from this aeolian dry deposition of desert dust, leads to fast re-oxidation of hydrogen sulfide produced during microbial sulfate reduction and limits preservation of reduced sulfur in the form of pyrite. We conclude that at these sites the sedimentary sulfur cycle may be defined as cryptic

People use less information than they think to make up their minds

A world where information is abundant promises unprecedented opportunities for information exchange. Seven studies suggest these opportunities work better in theory than in practice: People fail to anticipate how quickly minds change, believing that they and others will evaluate more evidence before making up their minds than they and others actually do. From evaluating peers, marriage prospects, and political candidates to evaluating novel foods, goods, and services, people consume far less information than expected before deeming things good or bad. Accordingly, people acquire and share too much information in impression-formation contexts: People overvalue long-term trials, overpay for decision aids, and overwork to impress others, neglecting the speed at which conclusions will form. In today’s information age, people may intuitively believe that exchanging ever-more information will foster better-informed opinions and perspectives—but much of this information may be lost on minds long made up