Terrain-based road vehicle localisation using particle filters

Dean, Adam J.; Martini, R.D.; Brennan, Sean

doi:10.1080/00423114.2010.493218

Cited by 33 publications

(13 citation statements)

References 19 publications

(23 reference statements)

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“…This technology has no cumulative errors and thus has achieve reasonable accuracy (~5m). The disadvantage, however, is that it can be easily affected by measurement noises [69], [78], [79].…”

Section: B Imu-based Localisationmentioning

confidence: 99%

Real-Time Performance-Focused Localization Techniques for Autonomous Vehicle: A Review

Lü

Smart

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Real-time, accurate, and robust localisation is critical for autonomous vehicles (AVs) to achieve safe, efficient driving, whilst real-time performance is essential for AVs to achieve their current position in time for decision making. To date, no review paper has quantitatively compared the real-time performance between different localisation techniques based on various hardware platforms and programming languages and analysed the relations among localisation methodologies, real-time performance and accuracy. Therefore, this paper discusses the state-of-the-art localisation techniques and analyses their overall performance in AV application. For further analysis, this paper firstly proposes a localisation algorithm operations capability (LAOC)-based equivalent comparison method to compare the relative computational complexity of different localisation techniques; then, it comprehensively discusses the relations among methodologies, computational complexity, and accuracy. Analysis results show that the computational complexity of localisation approaches differs by a maximum of about times, whilst accuracy varies by about 100 times. Vision-and data fusion-based localisation techniques have about 2-5 times potential for improving accuracy compared with lidar-based localisation. Lidar-and vision-based localisation can reduce computational complexity by improving image registration method efficiency. Data fusion-based localisation can achieve better real-time performance compared with lidar-and vision-based localisation because each standalone sensor does not need to develop a complex algorithm to achieve its best localisation potential. Vehicle-toeverything (V2X) technology can improve positioning robustness. Finally, the potential solutions and future orientations of AVs' localisation based on the quantitative comparison results are discussed.

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Section: B Imu-based Localisationmentioning

confidence: 99%

Real-Time Performance-Focused Localization Techniques for Autonomous Vehicle: A Review

Lü

Smart

et al. 2022

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…Particle degeneracy is a common problem when implementing a particle filter, which reduces the performance of the particle filter. Previous research on particle filter-based localization [4,[11][12][13]] using terrain information addressed particle degeneracy by increasing the particle numbers, which increased the computation burden. Moreover, only observation information at a current moment was used to update the particles.…”

Section: Problem Formulationmentioning

confidence: 99%

“…DTW has been shown to be the most precise measure for time-series data queries in most domains [16,40]. Even though previous localization research [11,34,37,38] has argued that DTW is not computationally efficient, nevertheless authors in [16] proposed an exact DTW algorithm whose sequential search is much faster than most alternatives. In our work, we adopted the DTW algorithm from [16] to do TSS matching on pitch data.…”

Section: Problem Formulationmentioning

confidence: 99%

A Real Time Localization System for Vehicles Using Terrain-Based Time Series Subsequence Matching

Zhang

Qian

et al. 2020

Remote Sensing

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Global Navigation Satellite Systems (GNSSs) are commonly used for positioning vehicles in open areas. Yet a GNSS frequently encounters loss of lock in urban areas. This paper presents a new real-time localization system using measurements from vehicle odometer data and data from an onboard inertial measurement unit (IMU), in the case of lacking GNSS information. A Dead Reckoning model integrates odometer data, IMU angular and velocity data to estimate the rough position of the vehicle. We then use an R-Tree structured reference road map of pitch data to boost spatial search efficiency. An optimized time series subsequence matching method matches the measured pitch data and the stored pitch data in reference road map for more accurate position estimation. The two estimated positions are fused using an extended Kalman filter model for final localization. The proposed localization system was tested for computational complexity with a median runtime of 12 ms, and for positioning accuracy with a median position error of 0.3 m.

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“…Using maps for vehicle localization and state estimation is not a new idea. Recent work by the authors [3,[25][26][27]] makes use of extremely compact maps of roads to localize a vehicle by using a measurement of its pitch angle alone. Alas, most of these studies tend to bring map information into a filter once every time step.…”

Section: Vehicle State Estimation With and Without Map Informationmentioning

confidence: 99%

Model-Based Vehicle State Estimation Using Previewed Road Geometry and Noisy Sensors

Brown

Brennan

2012

Volume 3: Renewable Energy Systems; Robotics; Robust Control; Single Track Vehicle Dynamics and Control; Stochastic Models, Con

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This paper proposes a method for using previewed road geometry from a high-fidelity map to improve estimates of planar vehicle states in the presence of unmodeled sensor bias errors. Using well-established, linear models for representing human driver behavior and for planar vehicle states, a causal link between previewed road geometry and vehicle states can be derived. Cast as an augmented, closed-loop linear system, the total driver-vehicle-road system's states are estimated using a Kalman filter. Estimation results from this filter using simulated noisy measurements of vehicle states and map-based measurements of previewed road geometry are compared to standard Kalman filters with identical measurements of vehicle states alone. The effects of errors in driver modeling, vehicle nonlinearity, and measurement disturbances on the estimator's fidelity are also examined and discussed.

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Terrain-based road vehicle localisation using particle filters

Cited by 33 publications

References 19 publications

Real-Time Performance-Focused Localization Techniques for Autonomous Vehicle: A Review

Real-Time Performance-Focused Localization Techniques for Autonomous Vehicle: A Review

A Real Time Localization System for Vehicles Using Terrain-Based Time Series Subsequence Matching

Model-Based Vehicle State Estimation Using Previewed Road Geometry and Noisy Sensors

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