Autonomous vehicle self-localization in urban environments based on 3D curvature feature points – Monte Carlo localization

Robotica ◽  
2021 ◽  
pp. 1-17
Author(s):  
Qi Liu ◽  
Xiaoguang Di ◽  
Binfeng Xu
Keyword(s):  
Monte Carlo ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Mapping Method ◽  
Urban Environments ◽  
Feature Points ◽  
Localization Algorithm ◽  
Accurate Localization ◽  
Localization Algorithms ◽  
Monte Carlo Localization

Abstract This paper proposes a map-based localization system for autonomous vehicle self-localization in urban environments, which is composed of a pose graph mapping method and 3D curvature feature points – Monte Carlo Localization algorithm (3DCF-MCL). The advantage of 3DCF-MCL is that it combines the high accuracy of the 3D feature points registration and the robustness of particle filter. Experimental results show that 3DCF-MCL can provide an accurate localization for autonomous vehicles with the 3D point cloud map that generated by our mapping method. Compared with other map-based localization algorithms, it demonstrates that 3DCF-MCL outperforms them.

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.

Accuracy analysis of BLE beacon-based localization in smart buildings

2021 ◽  
pp. 1-20
Author(s):  
Rosen Ivanov
Keyword(s):  
Use Case ◽  
Accuracy Analysis ◽  
Localization Algorithm ◽  
Computing Power ◽  
Smart Buildings ◽  
Localization Accuracy ◽  
Positioning Systems ◽  
Deployment Strategy ◽  
Accurate Localization ◽  
Localization Algorithms

The majority of services that deliver personalized content in smart buildings require accurate localization of their clients. This article presents an analysis of the localization accuracy using Bluetooth Low Energy (BLE) beacons. The aim is to present an approach to create accurate Indoor Positioning Systems (IPS) using algorithms that can be implemented in real time on platforms with low computing power. Parameters on which the localization accuracy mostly depends are analyzed: localization algorithm, beacons’ density, deployment strategy, and noise in the BLE channels. An adaptive algorithm for pre-processing the signals from the beacons is proposed, which aims to reduce noise in beacon’s data and to capture visitor’s dynamics. The accuracy of five range-based localization algorithms in different use case scenarios is analyzed. Three of these algorithms are specially designed to be less sensitive to noise in radio channels and require little computing power. Experiments conducted in a simulated and real environment show that using proposed algorithms the localization accuracy less than 1 m can be obtained.

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.

scholarly journals Navigating by touch: haptic Monte Carlo localization via geometric sensing and terrain classification

2021 ◽  
Vol 45 (6) ◽  
pp. 843-857
Author(s):  
Russell Buchanan ◽  
Jakub Bednarek ◽  
Marco Camurri ◽  
Michał R. Nowicki ◽  
Krzysztof Walas ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Estimation Error ◽  
Extreme Environments ◽  
Legged Robot ◽  
Terrain Classification ◽  
Localization Algorithm ◽  
Geometric Information ◽  
Contact Points ◽  
Monte Carlo Localization ◽  
Geometric Sensing

AbstractLegged robot navigation in extreme environments can hinder the use of cameras and lidar due to darkness, air obfuscation or sensor damage, whereas proprioceptive sensing will continue to work reliably. In this paper, we propose a purely proprioceptive localization algorithm which fuses information from both geometry and terrain type to localize a legged robot within a prior map. First, a terrain classifier computes the probability that a foot has stepped on a particular terrain class from sensed foot forces. Then, a Monte Carlo-based estimator fuses this terrain probability with the geometric information of the foot contact points. Results demonstrate this approach operating online and onboard an ANYmal B300 quadruped robot traversing several terrain courses with different geometries and terrain types over more than 1.2 km. The method keeps pose estimation error below 20 cm using a prior map with trained network and using sensing only from the feet, leg joints and IMU.

A Novel Power-Monitoring Strategy for Localization in Wireless Sensor Networks Using Antithetic Sampling Method

2020 ◽  
pp. 52-76
Author(s):  
Vasim Babu M.
Keyword(s):  
Monte Carlo ◽  
Sensor Node ◽  
Mapping Method ◽  
Computational Time ◽  
Localization Algorithm ◽  
Power Monitoring ◽  
Prime Objective ◽  
High Level ◽  
Power Mapping ◽  
Antithetic Sampling

The prime objective of this chapter is to develop a power-mapping localization algorithm based on Monte Carlo method using a discrete antithetic approach called Antithetic Markov Chain Monte Carlo (AMCMC). The chapter is focused on solving two major problems in WSN, thereby increasing the accuracy of the localization algorithm and discrete power control. Consecutively, the work is focused to reduce the computational time, while finding the location of the sensor. The model achieves the power controlling strategy using discrete power levels (CC2420 radio chip) which allocate the power, based on the event of each sensor node. By utilizing this discrete power mapping method, all the high-level parameters are considered for WSN. To improve the overall accuracy, the antithetic sampling is used to reduce the number of unwanted sampling, while identifying the sensor location in each transition state. At the final point, the accuracy is increased to 94% wherein nearly 24% of accuracy is increased compared to other MCL-based localization schemes.

scholarly journals Monte Carlo localization algorithm based on particle swarm optimization

Automatika ◽  
2019 ◽  
Vol 60 (4) ◽  
pp. 451-461 ◽  
Author(s):  
Cuiran Li ◽  
Jianli Xie ◽  
Wei Wu ◽  
Haoshan Tian ◽  
Yingxin Liang
Keyword(s):  
Monte Carlo ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Localization Algorithm ◽  
Swarm Optimization ◽  
Monte Carlo Localization

scholarly journals Self-adaptive Monte Carlo localization for mobile robots using range finders

Robotica ◽  
2011 ◽  
Vol 30 (2) ◽  
pp. 229-244 ◽  
Author(s):  
Lei Zhang ◽  
René Zapata ◽  
Pascal Lépinay
Keyword(s):  
Monte Carlo ◽  
Mobile Robots ◽  
Position Tracking ◽  
Localization Algorithm ◽  
Global Localization ◽  
Adaptive Monte Carlo ◽  
Local Sample ◽  
Monte Carlo Localization ◽  
Sample Set ◽  
Self Adaptive

SUMMARYIn order to achieve the autonomy of mobile robots, effective localization is a necessary prerequisite. In this paper, we propose an improved Monte Carlo localization algorithm using self-adaptive samples (abbreviated as SAMCL). By employing a pre-caching technique to reduce the online computational burden, SAMCL is more efficient than the regular MCL. Further, we define the concept of similar energy region (SER), which is a set of poses (grid cells) having similar energy with the robot in the robot space. By distributing global samples in SER instead of distributing randomly in the map, SAMCL obtains a better performance in localization. Position tracking, global localization and the kidnapped robot problem are the three sub-problems of the localization problem. Most localization approaches focus on solving one of these sub-problems. However, SAMCL solves all the three sub-problems together, thanks to self-adaptive samples that can automatically separate themselves into a global sample set and a local sample set according to needs. The validity and the efficiency of the SAMCL algorithm are demonstrated by both simulations and experiments carried out with different intentions. Extensive experimental results and comparisons are also given in this paper.

An Enhanced Monte Carlo Localization Algorithm for Mobile Node in Wireless Sensor Networks

2013 ◽  
Vol 401-403 ◽  
pp. 1800-1804 ◽  
Author(s):  
Shi Ping Fan ◽  
Yong Jiang Wen ◽  
Lin Zhou
Keyword(s):  
Wireless Sensor Networks ◽  
Monte Carlo ◽  
Sensor Networks ◽  
Mobile Node ◽  
Wireless Sensor ◽  
Distribution Area ◽  
Localization Algorithm ◽  
Monte Carlo Localization ◽  
Sample Points ◽  
Unknown Node

There are some common problems, such as low sampling efficiency and large amount of calculation, in mobile localization algorithm based on Monte Carlo localization (MCL) in wireless sensor networks. To improve these issues, an enhanced MCL algorithm is proposed. The algorithm uses the continuity of the nodes movement to predict the area where the unknown node may reach, constructs high posteriori density distribution area, adds the corresponding weights to the sample points which fall in different areas, and filters the sample points again by using the position relations between the unknown node and its one-hop neighbors which include anchor nodes and ordinary nodes. Simulation results show that the localization accuracy of the algorithm is superior to the traditional localization algorithm. Especially when the anchor node density is lower or the unknown nodes speed is higher, the algorithm has higher location accuracy.

scholarly journals Localization of isotropic and anisotropic wireless sensor networks in 2D and 3D fields

2021 ◽  
Author(s):  
Soumya J. Bhat ◽  
K. V. Santhosh
Keyword(s):  
Least Squares Method ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Smart Systems ◽  
Reference Node ◽  
Accurate Localization ◽  
Localization Algorithms ◽  
Node Ratio

AbstractInternet of Things (IoT) has changed the way people live by transforming everything into smart systems. Wireless Sensor Network (WSN) forms an important part of IoT. This is a network of sensor nodes that is used in a vast range of applications. WSN is formed by the random deployment of sensor nodes in various fields of interest. The practical fields of deployment can be 2D or 3D, isotropic or anisotropic depending on the application. The localization algorithms must provide accurate localization irrespective of the type of field. In this paper, we have reported a localization algorithm called Range Reduction Based Localization (RRBL). This algorithm utilizes the properties of hop-based and centroid methods to improve the localization accuracy in various types of fields. In this algorithm, the location unknown nodes identify the close-by neighboring nodes within a predefined threshold and localize themselves by identifying and reducing the probable range of existence from these neighboring nodes. The nodes which do not have enough neighbors are localized using the least squares method. The algorithm is tested in various irregular and heterogeneous conditions. The results are compared with a few state-of-the-art hop-based and centroid-based localization techniques. RRBL has shown an improvement in localization accuracy of 28% at 10% reference node ratio and 26% at 20% reference node ratio when compared with other localization algorithms.

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