scholarly journals A MODIFIED SAMPLING METHOD FOR LOCALIZATION ACCURACY IMPROVEMENT OF MONTE CARLO LOCALIZATION

2018 ◽  
Vol 18 (18) ◽  
pp. 1-9
Author(s):  
M. Awad-Allah ◽  
M. Abdelaziz ◽  
M. Shahin ◽  
F. Tolbah
Keyword(s):  
Monte Carlo ◽  
Sampling Method ◽  
Accuracy Improvement ◽  
Localization Accuracy ◽  
Monte Carlo Localization

scholarly journals Implicit Sampling for Path Integral Control, Monte Carlo Localization, and SLAM

2015 ◽  
Vol 137 (5) ◽  
Author(s):  
Matthias Morzfeld
Keyword(s):  
Monte Carlo ◽  
Numerical Experiments ◽  
High Probability ◽  
Sampling Method ◽  
Enhanced Sampling ◽  
Integral Control ◽  
Localization And Mapping ◽  
Monte Carlo Localization ◽  
Estimation And Control ◽  
And Control

Implicit sampling is a recently developed variationally enhanced sampling method that guides its samples to regions of high probability, so that each sample carries information. Implicit sampling may thus improve the performance of algorithms that rely on Monte Carlo (MC) methods. Here the applicability and usefulness of implicit sampling for improving the performance of MC methods in estimation and control is explored, and implicit sampling based algorithms for stochastic optimal control, stochastic localization, and simultaneous localization and mapping (SLAM) are presented. The algorithms are tested in numerical experiments where it is found that fewer samples are required if implicit sampling is used, and that the overall runtimes of the algorithms are reduced.

The Mobile Node Localization Algorithm Based on Monte Carlo

2013 ◽  
Vol 712-715 ◽  
pp. 1847-1850
Author(s):  
Jun Gang Zheng ◽  
Cheng Dong Wu ◽  
Zhong Tang Chen
Keyword(s):  
Monte Carlo ◽  
Mobile Node ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Node Localization ◽  
Localization Accuracy ◽  
Monte Carlo Localization ◽  
Simulation Results ◽  
Specialized Hardware ◽  
High Node

There exist some mobile node localization algoriths in wireless sensor netwok,which require high computation and specialized hardware and high node large density of beacon nodes.The Monte Carlo localization method has been studied and an improved Monte Carlo node localization has been proposed. Predicting the trajectory of the node by interpolation and combing sampling box to sampling. The method can improve the efficiency of sampling and accuracy. The simulation results show that the method has achieved good localization accuracy.

RSSI Based Monte-Carlo Localization Boxed Algorithm

2013 ◽  
Vol 397-400 ◽  
pp. 2048-2051
Author(s):  
Qiang Qu ◽  
Yong Xia ◽  
Yan Jiang ◽  
Xue Bo Chen
Keyword(s):  
Monte Carlo ◽  
Signal Strength ◽  
Location Error ◽  
Localization Accuracy ◽  
Practical Applications ◽  
Monte Carlo Localization ◽  
Estimation Precision ◽  
Simulation Results ◽  
Received Signal Strength Indication ◽  
Communication Radius

The maximum communication radius is applied to estimate the distance between one hop nodes in the MCB algorithm. However, the distance is generally less than the communication radius in practical applications which will result in the location error to some extent. So, the distance estimated by the received signal strength indication (RSSI) is used to substitute the communication radius in MCB algorithm to improve the estimation precision. The simulation results show that the average localization accuracy of the proposed algorithm is improved by about 14% comparing with the MCB algorithm.

scholarly journals Improved LiDAR Probabilistic Localization for Autonomous Vehicles Using GNSS

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3145 ◽  
Author(s):  
Miguel Ángel de Miguel ◽  
Fernando García ◽  
José María Armingol
Keyword(s):  
Monte Carlo ◽  
Autonomous Vehicles ◽  
Intelligent Systems ◽  
Autonomous Vehicle ◽  
Satellite System ◽  
Localization Accuracy ◽  
Monte Carlo Localization ◽  
Global Navigation Satellite ◽  
The University ◽  
Gnss Data

This paper proposes a method that improves autonomous vehicles localization using a modification of probabilistic laser localization like Monte Carlo Localization (MCL) algorithm, enhancing the weights of the particles by adding Kalman filtered Global Navigation Satellite System (GNSS) information. GNSS data are used to improve localization accuracy in places with fewer map features and to prevent the kidnapped robot problems. Besides, laser information improves accuracy in places where the map has more features and GNSS higher covariance, allowing the approach to be used in specifically difficult scenarios for GNSS such as urban canyons. The algorithm is tested using KITTI odometry dataset proving that it improves localization compared with classic GNSS + Inertial Navigation System (INS) fusion and Adaptive Monte Carlo Localization (AMCL), it is also tested in the autonomous vehicle platform of the Intelligent Systems Lab (LSI), of the University Carlos III de of Madrid, providing qualitative results.

Monte Carlo Localization for Humanoid Robot Navigation in Complex Indoor Environments

2014 ◽  
Vol 11 (02) ◽  
pp. 1441002 ◽  
Author(s):  
Armin Hornung ◽  
Stefan Oßwald ◽  
Daniel Maier ◽  
Maren Bennewitz
Keyword(s):  
Monte Carlo ◽  
Laser Scanner ◽  
Humanoid Robots ◽  
Indoor Environments ◽  
Observation Model ◽  
Proposal Distribution ◽  
Localization Accuracy ◽  
Monte Carlo Localization ◽  
High Level ◽  
Climbing Stairs

Accurate and reliable localization is a prerequisite for autonomously performing high-level tasks with humanoid robots. In this paper, we present a probabilistic localization method for humanoid robots navigating in arbitrary complex indoor environments using only onboard sensing, which is a challenging task. Inaccurate motion execution of biped robots leads to an uncertain estimate of odometry, and their limited payload constrains perception to observations from lightweight and typically noisy sensors. Additionally, humanoids do not walk on flat ground only and perform a swaying motion while walking, which requires estimating a full 6D torso pose. We apply Monte Carlo localization to globally determine and track a humanoid's 6D pose in a given 3D world model, which may contain multiple levels and staircases. We present an observation model to integrate range measurements from a laser scanner or a depth camera as well as attitude data and information from the joint encoders. To increase the localization accuracy, e.g., while climbing stairs, we propose a further observation model and additionally use monocular vision data in an improved proposal distribution. We demonstrate the effectiveness of our methods in extensive real-world experiments with a Nao humanoid. As the experiments illustrate, the robot is able to globally localize itself and accurately track its 6D pose while walking and climbing stairs.

scholarly journals IMPROVEMENT OF 3D MONTE CARLO LOCALIZATION USING A DEPTH CAMERA AND TERRESTRIAL LASER SCANNER

2015 ◽  
Vol XL-4/W5 ◽  
pp. 61-66 ◽  
Author(s):  
S. Kanai ◽  
R. Hatakeyama ◽  
H. Date
Keyword(s):  
Monte Carlo ◽  
Motion Estimation ◽  
Indoor Localization ◽  
Laser Scanner ◽  
Depth Camera ◽  
Terrestrial Laser Scanner ◽  
Localization Accuracy ◽  
Monte Carlo Localization ◽  
Inertia Sensor ◽  
Number Of Particles

Effective and accurate localization method in three-dimensional indoor environments is a key requirement for indoor navigation and lifelong robotic assistance. So far, Monte Carlo Localization (MCL) has given one of the promising solutions for the indoor localization methods. Previous work of MCL has been mostly limited to 2D motion estimation in a planar map, and a few 3D MCL approaches have been recently proposed. However, their localization accuracy and efficiency still remain at an unsatisfactory level (a few hundreds millimetre error at up to a few FPS) or is not fully verified with the precise ground truth. Therefore, the purpose of this study is to improve an accuracy and efficiency of 6DOF motion estimation in 3D MCL for indoor localization. Firstly, a terrestrial laser scanner is used for creating a precise 3D mesh model as an environment map, and a professional-level depth camera is installed as an outer sensor. GPU scene simulation is also introduced to upgrade the speed of prediction phase in MCL. Moreover, for further improvement, GPGPU programming is implemented to realize further speed up of the likelihood estimation phase, and anisotropic particle propagation is introduced into MCL based on the observations from an inertia sensor. Improvements in the localization accuracy and efficiency are verified by the comparison with a previous MCL method. As a result, it was confirmed that GPGPU-based algorithm was effective in increasing the computational efficiency to 10-50 FPS when the number of particles remain below a few hundreds. On the other hand, inertia sensor-based algorithm reduced the localization error to a median of 47mm even with less number of particles. The results showed that our proposed 3D MCL method outperforms the previous one in accuracy and efficiency.

scholarly journals Multi-sensor three-dimensional Monte Carlo localization for long-term aerial robot navigation

2017 ◽  
Vol 14 (5) ◽  
pp. 172988141773275 ◽  
Author(s):  
Francisco J Perez-Grau ◽  
Fernando Caballero ◽  
Antidio Viguria ◽  
Anibal Ollero
Keyword(s):  
Monte Carlo ◽  
Motion Tracking ◽  
Robot Navigation ◽  
Tracking System ◽  
Three Dimensional ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Localization Algorithm ◽  
Monte Carlo Localization ◽  
Motion Tracking System

This article presents an enhanced version of the Monte Carlo localization algorithm, commonly used for robot navigation in indoor environments, which is suitable for aerial robots moving in a three-dimentional environment and makes use of a combination of measurements from an Red,Green,Blue-Depth (RGB-D) sensor, distances to several radio-tags placed in the environment, and an inertial measurement unit. The approach is demonstrated with an unmanned aerial vehicle flying for 10 min indoors and validated with a very precise motion tracking system. The approach has been implemented using the robot operating system framework and works smoothly on a regular i7 computer, leaving plenty of computational capacity for other navigation tasks such as motion planning or control.

Sign in / Sign up

Export Citation Format

Share Document