Dynamic ICSP Graph Optimization Approach for Car-Like Robot Localization in Outdoor Environments

Wang, Zhan; Lambert, Alain; Zhang, Xun

doi:10.3390/computers8030063

Cited by 4 publications

(3 citation statements)

References 26 publications

(34 reference statements)

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“…Such state estimation is difficult due to the absence of knowledge on the initial condition x(0) as well as any other state, and because very few asynchronous non-linear observations are available. In particular, it has been shown in [48] and [49], that the problem is difficult to solve if the heading of the robot is not measured. As it has been said before, classical methods such as Bayesian approaches or particle filters badly behave in presence of few observations and are not suited to deal with large uncertainties because of inaccurate linearizations or convergence issues.…”

Section: A Problem Statementmentioning

confidence: 99%

Brunovsky Decomposition for Dynamic Interval Localization

Rohou

Jaulin

2023

IEEE Trans. Automat. Contr.

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This paper proposes a new set-membership method for estimating the trajectories of dynamical systems, when the states are completely unknown and only non-linear observations are available. The first part of the proposed method is symbolic and follows the decomposition of Brunovsky, i.e., it decomposes the set of differential equations describing the dynamical system into two blocks of constraints: one block gathers non-linear analytical equations that do not involve differential operators, while the other block is composed of linear chains of integrators. The second part of the method, that relies on the symbolic decomposition, is numerical and based on a contractor approach. It involves a specific optimal operator for narrowing the sets of feasible solutions. This approach is shown to be efficient on a difficult problem of dynamic localization of a mobile robot, without any prior knowledge about its states.

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Section: A Problem Statementmentioning

confidence: 99%

Brunovsky Decomposition for Dynamic Interval Localization

Rohou

Jaulin

2023

IEEE Trans. Automat. Contr.

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“…Kueviakoe et al [129], [130] introduced a real-time interval constraint propagation algorithm for on-road vehicle orientation correction using GPS, gyro, and odometer sensor data. Wang et al [131], [132] proposed to use interval constraint propagation technique to achieve localisation consistency and accuracy of a car-like robot by fusing DR, camera and map data. Both solutions considered the localisation problem as an interval constraint satisfaction problem.…”

Section: A Multi-sensor-based Data Fusion 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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“…It has been used to prove conjectures such as the existence of the Lorenz attractor [54], or that a given system is chaotic [20]. It has also been used for state estimation [31,40,1], localization [43,26,15,55] or SLAM [36].…”

Section: Introductionmentioning

confidence: 99%