An Efficient Fast-Mapping SLAM Method for UAS Applications Using Only Range Measurements

2016 ◽  
Vol 04 (02) ◽  
pp. 155-165 ◽  
Author(s):  
A. Torres-González ◽  
J. R. Martinez-de Dios ◽  
A. Jiménez-Cano ◽  
A. Ollero
Keyword(s):  
Experimental Validation ◽  
Field Experiments ◽  
Line Of Sight ◽  
Map Building ◽  
Localization Accuracy ◽  
Range Measurements ◽  
Localization And Mapping ◽  
Significant Interest ◽  
Local Map ◽  
Non Line Of Sight

This paper deals with 3D Simultaneous Localization and Mapping (SLAM), where the UAS uses only range measurements to build a local map of an unknown environment and to self-localize in that map. In the recent years Range Only (RO) SLAM has attracted significant interest, it is suitable for non line-of-sight conditions and bad lighting, being superior to visual SLAM in some problems. However, some issues constrain its applicability in practical cases, such as delays in map building and low map and UAS estimation accuracies. This paper proposes a 3D RO-SLAM scheme for UAS that specifically focuses on improving map building delays and accuracy levels without compromising efficiency in the consumption of resources. The scheme integrates sonar measurements together with range measurements between the robot and beacons deployed in the scenario. The proposed scheme presents two main advantages: (1) it integrates direct range measurements between the robot and the beacons and also range measurements between beacons — called inter-beacon measurements — which significantly reduce map building times and improve map and UAS localization accuracies; and (2) the SLAM scheme is endowed with a supervisory module that self-adapts the measurements that are integrated in SLAM reducing computational, bandwidth and energy consumption. Experimental validation in field experiments with an octorotor UAS showed that the proposed scheme improved map building times in 72%, map accuracy in 40% and UAS localization accuracy in 12%.

scholarly journals A Non-Parametric Propagation Condition Identification Method and Non-Line of Sight Mitigation Algorithm for Wireless Sensor Network

2016 ◽  
Vol 10 (1) ◽  
pp. 80-87 ◽  
Author(s):  
Hao Chu ◽  
Cheng-dong Wu
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Line Of Sight ◽  
Propagation Condition ◽  
Localization Accuracy ◽  
Chi Squared ◽  
Potential Applications ◽  
Unknown Node ◽  
Non Line Of Sight

The wireless sensor network (WSN) has received increasing attention since it has many potential applications such as the internet of things and smart city. The localization technology is critical for the application of the WSN. The obstacles induce the larger non-line of sight (NLOS) error and it may decrease the localization accuracy. In this paper, we mainly investigate the non-line of sight localization problem for WSN. Firstly, the Pearson's chi-squared testing is employed to identify the propagation condition. Secondly, the particle swarm optimization based localization method is proposed to estimate the position of unknown node. Finally the simulation experiments are implemented. The simulation results show that the proposed method owns higher localization accuracy when compared with other two methods.

scholarly journals Distributed Monocular SLAM for Indoor Map Building

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Ruwan Egodagamage ◽  
Mihran Tuceryan
Keyword(s):  
Mobile Agents ◽  
Single Agent ◽  
Map Building ◽  
Computational Framework ◽  
Critical Elements ◽  
Localization And Mapping ◽  
Monocular Slam ◽  
Map Generation ◽  
Indoor Tracking ◽  
Local Map

Utilization and generation of indoor maps are critical elements in accurate indoor tracking. Simultaneous Localization and Mapping (SLAM) is one of the main techniques for such map generation. In SLAM an agent generates a map of an unknown environment while estimating its location in it. Ubiquitous cameras lead to monocular visual SLAM, where a camera is the only sensing device for the SLAM process. In modern applications, multiple mobile agents may be involved in the generation of such maps, thus requiring a distributed computational framework. Each agent can generate its own local map, which can then be combined into a map covering a larger area. By doing so, they can cover a given environment faster than a single agent. Furthermore, they can interact with each other in the same environment, making this framework more practical, especially for collaborative applications such as augmented reality. One of the main challenges of distributed SLAM is identifying overlapping maps, especially when relative starting positions of agents are unknown. In this paper, we are proposing a system having multiple monocular agents, with unknown relative starting positions, which generates a semidense global map of the environment.

scholarly journals A Localization and Tracking Approach in NLOS Environment Based on Distance and Angle Probability Model

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4438 ◽  
Author(s):  
Xin Tian ◽  
Guoliang Wei ◽  
Jianhua Wang ◽  
Dianchen Zhang
Keyword(s):  
Monte Carlo ◽  
Kalman Filtering ◽  
Probability Model ◽  
Likelihood Estimation ◽  
Line Of Sight ◽  
Extended Kalman Filtering ◽  
Optimal Position ◽  
Localization Accuracy ◽  
Localization And Tracking ◽  
Non Line Of Sight

In this paper, an optimization algorithm is presented based on a distance and angle probability model for indoor non-line-of-sight (NLOS) environments. By utilizing the sampling information, a distance and angle probability model is proposed so as to identify the NLOS propagation. Based on the established model, the maximum likelihood estimation (MLE) method is employed to reduce the error of distance in the NLOS propagation. In order to reduce the computational complexity, a modified Monte Carlo method is applied to search the optimal position of the target. Moreover, the extended Kalman filtering (EKF) algorithm is introduced to achieve localization. The simulation and experimental results show the effectiveness of the proposed algorithm in the improvement of localization accuracy.

A Two-Step Optimizing Algorithm for TOA Real-Time Dynamic Localization in NLOS Environment

2013 ◽  
Vol 347-350 ◽  
pp. 3604-3608
Author(s):  
Shan Long ◽  
Zhe Cui ◽  
Fei Song
Keyword(s):  
Real Time ◽  
Weighted Least Squares ◽  
Line Of Sight ◽  
Wireless Localization ◽  
Localization Accuracy ◽  
Time Dynamic ◽  
Real Time Tracking ◽  
Step Algorithm ◽  
Optimizing Algorithm ◽  
Non Line Of Sight

Non-line-of-sight (NLOS) is one of the main factors that affect the ranging accuracy in wireless localization. This paper proposes a two-step optimizing algorithm for TOA real-time tracking in NLOS environment. Step one, use weighted least-squares (WLS) algorithm, combined with the NLOS identification informations, to mitigate NLOS bias. Step two, utilize Kalman filtering to optimize the localization results. Simulation results show that the proposed two-step algorithm can obtain better localization accuracy, especially when there are serious NLOS obstructions.

Accuracy Bounds for Wireless Localization Methods

2009 ◽  
pp. 380-405 ◽  
Author(s):  
Michael L. McGuire ◽  
Konstantinos N. Plataniotis
Keyword(s):  
Lower Bound ◽  
Location Estimation ◽  
Line Of Sight ◽  
Regularity Conditions ◽  
Node Localization ◽  
Localization Accuracy ◽  
Sensory Data ◽  
Line Of Sight Propagation ◽  
Cramer Rao Lower Bound ◽  
Non Line Of Sight

Node localization is an important issue for wireless sensor networks to provide context for collected sensory data. Sensor network designers need to determine if the desired level of localization accuracy is achievable from their network configuration and available measurements. The Cramér-Rao lower bound is used extensively for this purpose. This bound is loose since it uses only information from measurements in its calculations. Information, such as that from the sensor selection process, is not considered. In addition, non-line-of-sight radio propagation causes the regularity conditions of the Cramér-Rao lower bound to be violated. This chapter demonstrates the Weinstein-Weiss and extended Ziv-Zakai lower bounds for localization error which remain valid with non-line-of-sight propagation. These bounds also use all available information for bound calculations. It is demonstrated that these bounds are tight to actual estimator performance and may be used determine the available accuracy of location estimation from survey data collected in the network area.

Cooperative Improved Target Localization in harsh Environments using Direction of Arrival

2016 ◽  
Vol 3 (2) ◽  
pp. 420 ◽  
Author(s):  
Alireza Safaie ◽  
Reza Shahbazian ◽  
Seyed Ali Ghorashi
Keyword(s):  
Direction Of Arrival ◽  
Target Localization ◽  
Line Of Sight ◽  
Target Node ◽  
Performance Simulation ◽  
Localization Accuracy ◽  
Localization Method ◽  
Cooperative Methods ◽  
Mixed Line ◽  
Non Line Of Sight

<p>Target localization is an important issue for many applications in wireless sensor networks. However, it is rather difficult to maintain the localization accuracy in mixed line-of-sight (LOS) and non-line-of-sight (NLOS) environments as NLOS propagation leads to larger error than what LOS does. In this paper, we propose a new target localization method in mixed environments where NLOS is dominant and only one base node might be in LOS toward target. We use the cooperation between receiver nodes and the direction of arrival (DOA) of received signals to estimate the target’s location. The proposed cooperative target localization method tries to identify a base node that has LOS with respect to target node and use the LOS information for precise positioning of target node. We simulate the proposed method to analyze its performance. Simulation results confirm that our proposed method improves the localization accuracy on average by 20 percent in comparison with traditional cooperative methods.</p>

Integrating the Non-Line of Sight Launching System (NLOS-LS) in the United States Navy

2007 ◽  
Author(s):  
Jonathon Emis ◽  
Bryan Huang ◽  
Timothy Jones ◽  
Mei Li ◽  
Don Tumbocon
Keyword(s):  
United States ◽  
United States Navy ◽  
The United States ◽  
Line Of Sight ◽  
Non Line Of Sight

scholarly journals An Improved Localization Method for the Transition between Autonomous Underwater Vehicle Homing and Docking

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2468
Author(s):  
Ri Lin ◽  
Feng Zhang ◽  
Dejun Li ◽  
Mingwei Lin ◽  
Gengli Zhou ◽  
...  
Keyword(s):  
Data Exchange ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Optimization Method ◽  
Short Baseline ◽  
Localization Accuracy ◽  
Localization Method ◽  
Mapping Algorithm ◽  
Localization And Mapping ◽  
Update Rate

Docking technology for autonomous underwater vehicles (AUVs) involves energy supply, data exchange and navigation, and plays an important role to extend the endurance of the AUVs. The navigation method used in the transition between AUV homing and docking influences subsequent tasks. How to improve the accuracy of the navigation in this stage is important. However, when using ultra-short baseline (USBL), outliers and slow localization updating rates could possibly cause localization errors. Optical navigation methods using underwater lights and cameras are easily affected by the ambient light. All these may reduce the rate of successful docking. In this paper, research on an improved localization method based on multi-sensor information fusion is carried out. To improve the localization performance of AUVs under motion mutation and light variation conditions, an improved underwater simultaneous localization and mapping algorithm based on ORB features (IU-ORBSALM) is proposed. A nonlinear optimization method is proposed to optimize the scale of monocular visual odometry in IU-ORBSLAM and the AUV pose. Localization tests and five docking missions are executed in a swimming pool. The localization results indicate that the localization accuracy and update rate are both improved. The 100% successful docking rate achieved verifies the feasibility of the proposed localization method.

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