23 research outputs found
Graph Optimization Approach to Range-based Localization
In this paper, we propose a general graph optimization based framework for
localization, which can accommodate different types of measurements with
varying measurement time intervals. Special emphasis will be on range-based
localization. Range and trajectory smoothness constraints are constructed in a
position graph, then the robot trajectory over a sliding window is estimated by
a graph based optimization algorithm. Moreover, convergence analysis of the
algorithm is provided, and the effects of the number of iterations and window
size in the optimization on the localization accuracy are analyzed. Extensive
experiments on quadcopter under a variety of scenarios verify the effectiveness
of the proposed algorithm and demonstrate a much higher localization accuracy
than the existing range-based localization methods, especially in the altitude
direction
Vehicular Teamwork: Collaborative localization of Autonomous Vehicles
This paper develops a distributed collaborative localization algorithm based
on an extended kalman filter. This algorithm incorporates Ultra-Wideband (UWB)
measurements for vehicle to vehicle ranging, and shows improvements in
localization accuracy where GPS typically falls short. The algorithm was first
tested in a newly created open-source simulation environment that emulates
various numbers of vehicles and sensors while simultaneously testing multiple
localization algorithms. Predicted error distributions for various algorithms
are quickly producible using the Monte-Carlo method and optimization techniques
within MatLab. The simulation results were validated experimentally in an
outdoor, urban environment. Improvements of localization accuracy over a
typical extended kalman filter ranged from 2.9% to 9.3% over 180 meter test
runs. When GPS was denied, these improvements increased up to 83.3% over a
standard kalman filter. In both simulation and experimentally, the DCL
algorithm was shown to be a good approximation of a full state filter, while
reducing required communication between vehicles. These results are promising
in showing the efficacy of adding UWB ranging sensors to cars for collaborative
and landmark localization, especially in GPS-denied environments. In the
future, additional moving vehicles with additional tags will be tested in other
challenging GPS denied environments
A Review of Radio Frequency Based Localization for Aerial and Ground Robots with 5G Future Perspectives
Efficient localization plays a vital role in many modern applications of
Unmanned Ground Vehicles (UGV) and Unmanned aerial vehicles (UAVs), which would
contribute to improved control, safety, power economy, etc. The ubiquitous 5G
NR (New Radio) cellular network will provide new opportunities for enhancing
localization of UAVs and UGVs. In this paper, we review the radio frequency
(RF) based approaches for localization. We review the RF features that can be
utilized for localization and investigate the current methods suitable for
Unmanned vehicles under two general categories: range-based and fingerprinting.
The existing state-of-the-art literature on RF-based localization for both UAVs
and UGVs is examined, and the envisioned 5G NR for localization enhancement,
and the future research direction are explored
Research Trend Topic Area on Mobile Anchor Localization: A Systematic Mapping Study
Localization in a dynamic environment is one of the challenges in WSN localization involving dynamic sensor nodes or anchor nodes. Mobile anchors can be an efficient solution for the number of anchors in a 3-dimensional environment requiring more local anchors. The reliability of a localization system using mobile anchors is determined by various parameters such as energy efficiency, coverage, computational complexity, and cost. Various methods have been proposed by researchers to build a reliable mobile anchor localization system. This certainly shows the many research opportunities that can be carried out in mobile anchor localization. The many opportunities in this topic will be very confusing for researchers who want to research in this field in choosing a topic area early. However, until now there is still no paper that discusses systematic mapping studies that can provide information on topic areas and trends in the field of mobile anchor localization. A systematic Mapping Study (SMS) was conducted to determine the topic area and its trends, influential authors, and produce modeling topics and trends from the resulting modeling topics. This SMS can be a solution for researchers who are interested in research in the field of mobile anchor localization in determining the research topics they are interested in for further research. This paper gives information on the mobile anchor research area, the author who has influenced mobile anchor localization research, and the topic modeling and trend that potentially promissing research in the future. The SMS includes a chronology of publications from 2017-2022, bibliometric co-occurrence, co-author analysis, topic modeling, and trends. The results show that the development of mobile anchor localization publications is still developing until 2022. There are 10 topic models with 6 of them included in the promising topic. The results of this SMS can be used as preliminary research from the literacy stage, namely Systematic Literature Review (SLR)
A Review of Radio Frequency Based Localisation for Aerial and Ground Robots with 5G Future Perspectives
Efficient localisation plays a vital role in many modern applications of Unmanned Ground Vehicles (UGV) and Unmanned Aerial Vehicles (UAVs), which contributes to improved control, safety, power economy, etc. The ubiquitous 5G NR (New Radio) cellular network will provide new opportunities to enhance the localisation of UAVs and UGVs. In this paper, we review radio frequency (RF)-based approaches to localisation. We review the RF features that can be utilized for localisation and investigate the current methods suitable for Unmanned Vehicles under two general categories: range-based and fingerprinting. The existing state-of-the-art literature on RF-based localisation for both UAVs and UGVs is examined, and the envisioned 5G NR for localisation enhancement, and the future research direction are explored
Decentralized Collaborative Localization Using Ultra-Wideband Ranging
This thesis summarizes the development of a collaborative localization algorithm simulation environment and the implementation of collaborative localization using Ultra-Wideband ranging in autonomous vehicles. In the developed simulation environment, multi-vehicle scenarios are testable with various sensor combinations and configurations. The simulation emulates the networking required for collaborative localization and serves as a platform for evaluating algorithm performance using Monte Carlo analysis. Monte-Carlo simulations were run using a number of situations and vehicles to test the efficacy of UWB sensors in decentralized collaborative localization as well as landmark measurements within an extended Kalman filter. Improvements from adding Ultra-Wideband ranging were shown in all simulated environments, with landmarks offering additional improvements to collaborative localization, and with the most significant accuracy improvements seen in GNSS-denied environments. Physical experiments were run using a by-wire GEM e6 from Autonomous Stuff in an urban environment in both collaborative and landmark setups. Due to higher than expected INS certainty, adding UWB measurements showed smaller improvements than simulations. Improvements of 9.2 to 12.1% were shown through the introduction of Ultra-Wideband ranging measurements in a decentralized collaborative localization algorithm. Improvements of 30.6 to 83.3% were shown in using UWB ranging measurements to landmarks in an Extended Kalman Filter for street crossing and tunnel environments respectively. These results are similar to the simulated data, and are promising in showing the efficacy of adding UWB ranging sensors to cars for collaborative and landmark localization, especially in GNSS-denied environments. In the future, additional moving vehicles with additional tags will be tested and further evaluations of the UWB ranging modules will be performed
confined spaces industrial inspection with micro aerial vehicles and laser range finder localization
This work addresses the problem of semi-automatic inspection and navigation in confined environments. A system that overcomes many challenges at the state of the art is presented. It comprises a mu..
UWB-based system for UAV Localization in GNSS-Denied Environments: Characterization and Dataset
Small unmanned aerial vehicles (UAV) have penetrated multiple domains over
the past years. In GNSS-denied or indoor environments, aerial robots require a
robust and stable localization system, often with external feedback, in order
to fly safely. Motion capture systems are typically utilized indoors when
accurate localization is needed. However, these systems are expensive and most
require a fixed setup. Recently, visual-inertial odometry and similar methods
have advanced to a point where autonomous UAVs can rely on them for
localization. The main limitation in this case comes from the environment, as
well as in long-term autonomy due to accumulating error if loop closure cannot
be performed efficiently. For instance, the impact of low visibility due to
dust or smoke in post-disaster scenarios might render the odometry methods
inapplicable. In this paper, we study and characterize an ultra-wideband (UWB)
system for navigation and localization of aerial robots indoors based on
Decawave's DWM1001 UWB node. The system is portable, inexpensive and can be
battery powered in its totality. We show the viability of this system for
autonomous flight of UAVs, and provide open-source methods and data that enable
its widespread application even with movable anchor systems. We characterize
the accuracy based on the position of the UAV with respect to the anchors, its
altitude and speed, and the distribution of the anchors in space. Finally, we
analyze the accuracy of the self-calibration of the anchors' positions.Comment: Accepted to the 2020 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2020