Particle filters for positioning, navigation, and tracking (original) (raw)
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International Journal of Electrical and Computer Engineering (IJECE), 2023
This article presents a new method for land vehicle navigation using global positioning system (GPS), dead reckoning sensor (DR), and digital road map information, particularly in urban environments where GPS failures can occur. The odometer sensors and map measure can be used to provide continuous navigation and correct the vehicle location in the presence of GPS masking. To solve this estimation problem for vehicle navigation, we propose to use particle filtering for GPS/odometer/map integration. The particle filter is a method based on the Bayesian estimation technique and the Monte Carlo method, which deals with non-linear models and is not limited to Gaussian statistics. When the GPS sensor cannot provide a location due to the number of satellites in view, the filter fuses the limited GPS pseudo-range data to enhance the vehicle positioning. The developed filter is then tested in a transportation network scenario in the presence of GPS failures, which shows the advantages of the proposed approach for vehicle location compared to the extended Kalman filter. This is an open access article under the CC BY-SA license.
Application of particle filters to a map-matching algorithm
Gyroscopy and Navigation, 2011
Key words: land vehicle navigation, map-matching, sequential Monte Carlo method, particle filtering This paper presents the numerical probabilistic approach to map-matching problem within the framework of Bayesian theory. The proposed solution is based on sequential Monte Carlo method, so called particle filtering. This algorithm can be adapted for implementation on real-time portable car navigation systems equipped with GPS or dead reckoning sensors. The algorithm reliability and accuracy performance was investigated using simulated data and data from real-world driving tests in urban environment.
APPLICATION OF PARTICLE FILTERS TO MAP-MATCHING ALGORITHM
This paper presents the numerical probabilistic approach to map-matching problem within the framework of Bayesian theory. The proposed solution is based on sequential Monte Carlo method, so called particle filtering. This algorithm can be adapted for implementation on real-time portable car navigation systems equipped with GPS or dead reckoning sensors. The algorithm reliability and accuracy performance was investigated using simulated data and data from real-world driving tests in urban environment.
Map-Aware Particle Filter for Localization
2018 IEEE International Conference on Robotics and Automation (ICRA), 2018
This work presents a method to improve vehicle localization by using the information from a prior occupancy grid to bound the possible poses. The method, named Map-Aware Particle Filter, uses a nonlinear approach to mapmatching that can be integrated into a particle filter framework for localization. Each particle is re-weighted based on the validity of its current position in the map. In addition, we buffer the trajectory followed by the vehicle and then append it to each particle's pose. We then quantify the overlap between the trajectory and the map's free space. This serves as a measure of each particle's validity given the trajectory and the shape of the map. We evaluated the method by performing experiments with different types of localization sensors: First, (i) we significantly reduced the drift inherent to dead reckoning. By only using wheel odometry and map information we achieved loop closure over a distance of approximately 3 km. We also (ii) increased the accuracy of GPS localization. Finally, (iii) we fused a fragile 2D LiDAR localization with the map information. The resulting system had a higher robustness and managed to close the loop in an outdated map where it had failed before.
Circular particle fusion filter applied to map matching
IET Intelligent Transport Systems, 2017
Navigation in constrained areas such as ports or dense urban environments is often exposed to global navigation satellite system (GNSS) satellites masking caused by the infrastructures. In this case, the GNSS positioning is inaccurate or unavailable and proprioceptive sensors are generally used to temporarily localise the vehicle on a map. However, the drift of these sensors rapidly causes the navigation system to fail. In this study, the navigation is computed using magnetometer and GNSS observations defined in an absolute reference frame. The heading measurements are coupled with a digital map of the road network in order to localise the vehicle in a map matching process. The contribution of this study is the proposition of a particle filter that fuses the position and direction observations to estimate the vehicle position. In this context, when the GNSS signal is masked, the observations of direction are used to compute the vehicle position. The proposed filter is defined in both circular and linear domains in order to take into account the nature of the observations. The proposed approach is assessed on real and synthetic data.
Real-Time Autonomous Vehicle Localization Based on Particle and Unscented Kalman Filters
Journal of Control, Automation and Electrical Systems, 2021
In this paper, a real-time Monte Carlo localization (RT_MCL) method for autonomous cars is proposed. Unlike the other localization approaches, the balanced treatment of both pose estimation accuracy and its real-time performance is the main contribution. The RT_MCL method is based on the fusion of lidar and radar measurement data for object detection, a polelike landmarks probabilistic map and a tailored particle flter for pose estimation. The lidar and radar are fused using the unscented Kalman flter (UKF) to provide pole-like static-object pose estimations that are well-suited to serve as landmarks for vehicle localization in urban environments. These pose estimations are then clustered using the grid-based density-based spatial clustering of applications with noise algorithm to represent each pole landmark in the form of a source-point model to reduce computational cost and memory requirements. A reference map that includes pole landmarks is generated offline and extracted from a 3-D lidar to be used by a carefully designed particle filter for accurate ego-car localization. The particle flter is initialized by the fused GPS+IMU measurements and used an ego-car motion model to predict the states of the particles. The data association between the estimated landmarks by the UKF and that in the reference map is performed using the iterative closest point algorithm. The RT_MCL is implemented using the high-performance language C++ and utilizes highly optimized math and optimization libraries for the best real-time performance. Extensive simulation studies have been carried out to evaluate the performance of the RT_MCL in both longitudinal localization and lateral localization.
A Comparative Analysis of Particle Filter Based Localization Methods
Lecture Notes in Computer Science, 2007
The knowledge of the pose and the orientation of a mobile robot in its operating environment is of utmost importance for an autonomous robot. Techniques solving this problem are referred to as self-localization algorithms. Self-localization is a deeply investigated field in mobile robotics, and many effective solutions have been proposed. In this context, Monte Carlo Localization (MCL) is one of the most popular approaches, and represents a good tradeoff between robustness and accuracy. The basic underlying principle of this family of approaches is using a Particle Filter for tracking a probability distribution of the possible robot poses.
Highway evaluation of terrain-aided localization using particle filters
2008
This work evaluates a real-time algorithm to localize a vehicle on a highway in the direction of travel without the use of GPS. The algorithm uses a particle filter to estimate vehicle position along a map of road grade using real-time pitch measurements from an in-vehicle pitch sensor as the input. Experiments over 60 kilometers along Interstate I-80 and US Route 220 in Pennsylvania are used to demonstrate the algorithm, observe the speed of convergence, and evaluate several methods of implementation.
Integrated Navigation System Using Sigma-Point Kalman Filter and Particle Filter
2008
The paper describes integration of Inertial Navigation System (INS) and Global Positioning System (GPS) navigation systems using new approaches for navigation information processing based on efficient SigmaPoint Kalman filtering and Particle filtering. The paper points out the inherent shortcomings in using the linearization techniques in standard Kalman filters (like Linearized Kalman filter or Extended Kalman filter) and presents, as an alternative, a family of improved derivativeless nonlinear filters. The integrated system was created in a simulation environment. An original contribution of the work consists in creation of models in the simulation environment to confirm the algorithms. The work results are represented in a chart and supported by statistical data to confirm the rightness of the algorithms developed. 1.0 INTRODUCTION Accurate and reliable navigation systems will have an important role for enhanced military capabilities in the coming years. The INS and GPS are wide...
Rapid Localization and Mapping Method Based on Adaptive Particle Filters
Sensors
With the development of autonomous vehicles, localization and mapping technologies have become crucial to equip the vehicle with the appropriate knowledge for its operation. In this paper, we extend our previous work by prepossessing a localization and mapping architecture for autonomous vehicles that do not rely on GPS, particularly in environments such as tunnels, under bridges, urban canyons, and dense tree canopies. The proposed approach is of two parts. Firstly, a K-means algorithm is employed to extract features from LiDAR scenes to create a local map of each scan. Then, we concatenate the local maps to create a global map of the environment and facilitate data association between frames. Secondly, the main localization task is performed by an adaptive particle filter that works in four steps: (a) generation of particles around an initial state (provided by the GPS); (b) updating the particle positions by providing the motion (translation and rotation) of the vehicle using an ...