606 research outputs found
Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition
The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future
Competing in the RoboCup Rescue Robot League
RoboCup Rescue is an international competition in which robots compete to find disaster victims in a simulated earthquake environment. It features both a Rescue Simulation League (RSL) which is entirely computer simulated, and a Rescue Robot League (RRL) with real robots and a test arena. This paper will describe the experience gained sending an undergraduate team to compete in the Rescue Robot League at the RoboCup German Open in 2008 and 2009. The design of the test arena and the rules of the competition will be outlined; as will the approaches taken by different teams; and the competition results
Map matching and heuristic elimination of gyro drift for personal navigation systems in GPS-denied conditions
This paper introduces a method for the substantial reduction of heading errors in inertial navigation systems used under GPS-denied conditions. Presumably, the method is applicable for both vehicle-based and personal navigation systems, but experiments were performed only with a personal navigation system called 'personal dead reckoning' (PDR). In order to work under GPS-denied conditions, the PDR system uses a foot-mounted inertial measurement unit (IMU). However, gyro drift in this IMU can cause large heading errors after just a few minutes of walking. To reduce these errors, the map-matched heuristic drift elimination (MAPHDE) method was developed, which estimates gyro drift errors by comparing IMU-derived heading to the direction of the nearest street segment in a database of street maps. A heuristic component in this method provides tolerance to short deviations from walking along the street, such as when crossing streets or intersections. MAPHDE keeps heading errors almost at zero, and, as a result, position errors are dramatically reduced. In this paper, MAPHDE was used in a variety of outdoor walks, without any use of GPS. This paper explains the MAPHDE method in detail and presents experimental results.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90785/1/0957-0233_22_2_025205.pd
Feature engineering workflow for activity recognition from synchronized inertial measurement units
The ubiquitous availability of wearable sensors is responsible for driving
the Internet-of-Things but is also making an impact on sport sciences and
precision medicine. While human activity recognition from smartphone data or
other types of inertial measurement units (IMU) has evolved to one of the most
prominent daily life examples of machine learning, the underlying process of
time-series feature engineering still seems to be time-consuming. This lengthy
process inhibits the development of IMU-based machine learning applications in
sport science and precision medicine. This contribution discusses a feature
engineering workflow, which automates the extraction of time-series feature on
based on the FRESH algorithm (FeatuRe Extraction based on Scalable Hypothesis
tests) to identify statistically significant features from synchronized IMU
sensors (IMeasureU Ltd, NZ). The feature engineering workflow has five main
steps: time-series engineering, automated time-series feature extraction,
optimized feature extraction, fitting of a specialized classifier, and
deployment of optimized machine learning pipeline. The workflow is discussed
for the case of a user-specific running-walking classification, and the
generalization to a multi-user multi-activity classification is demonstrated.Comment: Multi-Sensor for Action and Gesture Recognition (MAGR), ACPR 2019
Workshop, Auckland, New Zealan
Providing location everywhere
Anacleto R., Figueiredo L., Novais P., Almeida A., Providing Location Everywhere, in Progress in Artificial Intelligence, Antunes L., Sofia Pinto H. (eds), Lecture Notes in Artificial Intelligence 7026, Springer-Verlag, ISBN 978-3-540-24768-2, (Proceedings of the 15th Portuguese conference on Artificial Intelligence - EPIA 2011, Lisboa, Portugal), pp 15-28, 2011.The ability to locate an individual is an essential part of many applications, specially the mobile ones. Obtaining this location
in an open environment is relatively simple through GPS (Global Positioning System), but indoors or even in dense environments this type of
location system doesn’t provide a good accuracy. There are already systems that try to suppress these limitations, but most of them need the
existence of a structured environment to work. Since Inertial Navigation Systems (INS) try to suppress the need of a structured environment we
propose an INS based on Micro Electrical Mechanical Systems (MEMS) that is capable of, in real time, compute the position of an individual everywhere
HINNet + HeadSLAM: robust inertial navigation with machine learning for long-term stable tracking
In recent years, human position tracking with wearable sensors has been rapidly developed and shown great
potential for applications within healthcare, smart homes, sports, and emergency services. Unlike tracking researches
with sensors on the foot, human positioning studies with head-mounted sensors are fewer and still remain problems that
have not been solved. We have proposed two studies solve part of the problems separately: HINNet is able to track
people with free head rotations; HeadSLAM allows long-term tracking with stable errors. In this paper, to allow free head
rotations meanwhile support long-term tracking, HINNet is combined with HeadSLAM and tested. The result shows that
the combination could effectively distinguish head rotations and keep a low and stable position error in long-term tracking,
with an absolute trajectory error (ATE) of 2.69m and relative trajectory error (RTE) of 3.52m
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