Related papers: Machine Learning for Indoor Localization Using Mob…
Location-aware applications play an increasingly critical role in everyday life. However, satellite-based localization (e.g., GPS) has limited accuracy and can be unusable in dense urban areas and indoors. We introduce an image-based global…
The main focus of beam alignment is to find the optimal beam which yields the largest received signal strength (RSS) with faster speed.In this paper, we demonstrate an efficient beam alignment scheme with our testbed. The algorithm we…
Positional estimation is of great importance in the public safety sector. Emergency responders such as fire fighters, medical rescue teams, and the police will all benefit from a resilient positioning system to deliver safe and effective…
Today, mobile robots are expected to carry out increasingly complex tasks in multifarious, real-world environments. Often, the tasks require a certain semantic understanding of the workspace. Consider, for example, spoken instructions from…
In this work we study indoor scene object placement. Given a 3D indoor scene and an object, the task is to predict placement locations within the scene. Empirical observations of data-driven approaches to the problem show their tendency to…
Indoor localization is a challenging task. Compared to outdoor environments where GPS is dominant, there is no robust and almost-universal approach. Recently, machine learning (ML) has emerged as the most promising approach for achieving…
Efficient learning from demonstration for long-horizon tasks remains an open challenge in robotics. While significant effort has been directed toward learning trajectories, a recent resurgence of object-centric approaches has demonstrated…
The continual proliferation of mobile devices has encouraged much effort in using the smartphones for indoor positioning. This article is dedicated to review the most recent and interesting smartphones based indoor navigation systems,…
Self-localization is an important technology for automating bulldozers. Conventional bulldozer self-localization systems rely on RTK-GNSS (Real Time Kinematic-Global Navigation Satellite Systems). However, RTK-GNSS signals are sometimes…
Although indoor localization has been a wide researched topic, obtained results may not fit the requirements that some domains need. Most approaches are not able to precisely localize a fast moving object even with a complex installation,…
Precise indoor localization is an increasingly demanding requirement for various emerging applications, like Virtual/Augmented reality and personalized advertising. Current indoor environments are equipped with pluralities of WiFi access…
Fingerprinting-based indoor localization is an emerging application domain for enhanced positioning and tracking of people and assets within indoor locales. The superior pairing of ubiquitously available WiFi signals with computationally…
We present a practical approach for processing mobile sensor time series data for continual deep learning predictions. The approach comprises data cleaning, normalization, capping, time-based compression, and finally classification with a…
We propose a localization algorithm for wireless sensor networks, which is simple in design, does not involve significant overhead and yet provides acceptable position estimates of sensor nodes. The algorithm uses settled nodes as beacon…
We learn, in an unsupervised way, an embedding from sequences of radar images that is suitable for solving the place recognition problem with complex radar data. Our method is based on invariant instance feature learning but is tailored for…
Bluetooth is a promising short-range radio network technology. We present a low cost and easily deployed, scalable infrastructure for indoor location-based computing of mobile devices based on Bluetooth technology. The system consists of 2…
Humans and robots working together in an environment to enhance human performance is the aim of Industry 5.0. Although significant progress in outdoor positioning has been seen, indoor positioning remains a challenge. In this paper, we…
Compared to LiDAR-based localization methods, which provide high accuracy but rely on expensive sensors, visual localization approaches only require a camera and thus are more cost-effective while their accuracy and reliability typically is…
In this paper, we present an acoustic localization system for multiple devices. In contrast to systems which localise a device relative to one or several anchor points, we focus on the joint localisation of several devices relative to each…
Localization is a fundamental enabler technology for many applications, like vehicular networks, IoT, and even medicine. While Global Navigation Satellite Systems solutions offer great performance, they are unavailable in scenarios like…