3,625 research outputs found
GP-Localize: Persistent Mobile Robot Localization using Online Sparse Gaussian Process Observation Model
Central to robot exploration and mapping is the task of persistent
localization in environmental fields characterized by spatially correlated
measurements. This paper presents a Gaussian process localization (GP-Localize)
algorithm that, in contrast to existing works, can exploit the spatially
correlated field measurements taken during a robot's exploration (instead of
relying on prior training data) for efficiently and scalably learning the GP
observation model online through our proposed novel online sparse GP. As a
result, GP-Localize is capable of achieving constant time and memory (i.e.,
independent of the size of the data) per filtering step, which demonstrates the
practical feasibility of using GPs for persistent robot localization and
autonomy. Empirical evaluation via simulated experiments with real-world
datasets and a real robot experiment shows that GP-Localize outperforms
existing GP localization algorithms.Comment: 28th AAAI Conference on Artificial Intelligence (AAAI 2014), Extended
version with proofs, 10 page
A review of sensor technology and sensor fusion methods for map-based localization of service robot
Service robot is currently gaining traction, particularly in hospitality, geriatric care and healthcare industries. The navigation of service robots requires high adaptability, flexibility and reliability. Hence, map-based navigation is suitable for service robot because of the ease in updating changes in environment and the flexibility in determining a new optimal path. For map-based navigation to be robust, an accurate and precise localization method is necessary. Localization problem can be defined as recognizing the robot’s own position in a given environment and is a crucial step in any navigational process. Major difficulties of localization include dynamic changes of the real world, uncertainties and limited sensor information. This paper presents a comparative review of sensor technology and sensor fusion methods suitable for map-based localization, focusing on service robot applications
Radar-on-Lidar: metric radar localization on prior lidar maps
Radar and lidar, provided by two different range sensors, each has pros and
cons of various perception tasks on mobile robots or autonomous driving. In
this paper, a Monte Carlo system is used to localize the robot with a rotating
radar sensor on 2D lidar maps. We first train a conditional generative
adversarial network to transfer raw radar data to lidar data, and achieve
reliable radar points from generator. Then an efficient radar odometry is
included in the Monte Carlo system. Combining the initial guess from odometry,
a measurement model is proposed to match the radar data and prior lidar maps
for final 2D positioning. We demonstrate the effectiveness of the proposed
localization framework on the public multi-session dataset. The experimental
results show that our system can achieve high accuracy for long-term
localization in outdoor scenes
Towards Odor-Sensitive Mobile Robots
J. Monroy, J. Gonzalez-Jimenez, "Towards Odor-Sensitive Mobile Robots", Electronic Nose Technologies and Advances in Machine Olfaction, IGI Global, pp. 244--263, 2018, doi:10.4018/978-1-5225-3862-2.ch012
Versión preprint, con permiso del editorOut of all the components of a mobile robot, its sensorial system is undoubtedly among the most critical
ones when operating in real environments. Until now, these sensorial systems mostly relied on range
sensors (laser scanner, sonar, active triangulation) and cameras. While electronic noses have barely
been employed, they can provide a complementary sensory information, vital for some applications, as
with humans. This chapter analyzes the motivation of providing a robot with gas-sensing capabilities
and also reviews some of the hurdles that are preventing smell from achieving the importance of other
sensing modalities in robotics. The achievements made so far are reviewed to illustrate the current status
on the three main fields within robotics olfaction: the classification of volatile substances, the spatial
estimation of the gas dispersion from sparse measurements, and the localization of the gas source within
a known environment
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
Long-term experiments with an adaptive spherical view representation for navigation in changing environments
Real-world environments such as houses and offices change over time, meaning that a mobile robot’s map will become out of date. In this work, we introduce a method to update the reference views in a hybrid metric-topological map so that a mobile robot can continue to localize itself in a changing environment. The updating mechanism, based on the multi-store model of human memory, incorporates a spherical metric representation of the observed visual features for each node in the map, which enables the robot to estimate its heading and navigate using multi-view geometry, as well as representing the local 3D geometry of the environment. A series of experiments demonstrate the persistence performance of the proposed system in real changing environments, including analysis of the long-term stability
RFID Localisation For Internet Of Things Smart Homes: A Survey
The Internet of Things (IoT) enables numerous business opportunities in
fields as diverse as e-health, smart cities, smart homes, among many others.
The IoT incorporates multiple long-range, short-range, and personal area
wireless networks and technologies into the designs of IoT applications.
Localisation in indoor positioning systems plays an important role in the IoT.
Location Based IoT applications range from tracking objects and people in
real-time, assets management, agriculture, assisted monitoring technologies for
healthcare, and smart homes, to name a few. Radio Frequency based systems for
indoor positioning such as Radio Frequency Identification (RFID) is a key
enabler technology for the IoT due to its costeffective, high readability
rates, automatic identification and, importantly, its energy efficiency
characteristic. This paper reviews the state-of-the-art RFID technologies in
IoT Smart Homes applications. It presents several comparable studies of RFID
based projects in smart homes and discusses the applications, techniques,
algorithms, and challenges of adopting RFID technologies in IoT smart home
systems.Comment: 18 pages, 2 figures, 3 table
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