A Low-Cost Consistent Vehicle Localization Based on Interval Constraint Propagation

Wang, Zhan; Lambert, Alain

doi:10.1155/2018/2713729

Cited by 11 publications

(8 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is because the DIGO method could take advantage of measurements from other landmarks to benefit the current landmark. According to our previous study [30], the bigger the parallax is, the better the landmark's estimation will be. During each observation, different landmarks may have different parallax.…”

Section: Numerical Resultsmentioning

confidence: 93%

“…In practical application, a compromise between the mapping precision and processing time should be made. To manifest the improvement of our proposed DIGO method with the traditional one [30] that solves the ICSPs locally (Local ICP Contraction: LIC), we make a comparison of the final estimated landmark's position. For the sake of simplicity, the two methods perform independently with the same data source and the final mapping results are compared by calculating the improvement factor η in terms of axis x, y and z:…”

Section: Numerical Resultsmentioning

confidence: 99%

“…As we presented in our previous study [30], the generated ICSPs can be solved independently by local consistency ICP algorithms (Forward/Backward, HC3, HC4, etc.). However, we note that, at each timestep k, the generated ICSPs associated with each observed landmark are linked together by two links.…”

Section: Graph Architecturementioning

confidence: 99%

See 2 more Smart Citations

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

2019

Self Cite

View full text Add to dashboard Cite

Localization has been regarded as one of the most fundamental problems to enable a mobile robot with autonomous capabilities. Probabilistic techniques such as Kalman or Particle filtering have long been used to solve robotic localization and mapping problem. Despite their good performance in practical applications, they could suffer inconsistency problems. This paper presents an Interval Constraint Satisfaction Problem (ICSP) graph based methodology for consistent car-like robot localization in outdoor environments. The localization problem is cast into a two-stage framework: visual teach and repeat. During a teaching phase, the interval map is built when a robot navigates around the environment with GPS-support. The map is then used for real-time ego-localization as the robot repeats the path autonomously. By dynamically solving the ICSP graph via Interval Constraint Propagation (ICP) techniques, a consistent and improved localization result is obtained. Both numerical simulation results and real data set experiments are presented, showing the soundness of the proposed method in achieving consistent localization.

show abstract

Section: Numerical Resultsmentioning

confidence: 93%

Section: Numerical Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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

2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…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.

View full text Add to dashboard Cite

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.

show abstract

“…e global path consists of multiple waypoints that the vehicle can pass through intersections. e localization data are obtained through the global positioning system or by the simultaneous localization and mapping technique [4][5][6]. However, in an unstructured environment with narrow roads or complex obstacles, the localization data can be inaccurate, which increases the possibility of collision with obstacles.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Branch Road Detection for Intersection Navigation in Unstructured Environment Using Multi-Task Network

Ahn

Lee

Kim

et al. 2022

Journal of Advanced Transportation

View full text Add to dashboard Cite

Autonomous vehicles need a driving method to be less dependent on localization data to navigate intersections in unstructured environments because these data may not be accurate in such environments. Methods of distinguishing branch roads existing at intersections using vision and applying them to intersection navigation have been studied. Model-based detection methods recognize patterns of the branch roads, but are sensitive to sensor noise and difficult to apply to various complex situations. Therefore, this study proposes a method for detecting the branch roads at the intersection using deep learning. The proposed multi-task deep neural network distinguishes the branch road into a shape of rotated bounding boxes and also recognizes the drivable area to consider obstacles inside the box. Through the output of the network, an occupancy grid map consisting of one branch road at an intersection is obtained, which can be used as an input to the existing motion-planning algorithms that do not consider intersections. Experiments in real environments show that the proposed method detected the branch roads more accurately than the model-based detection method, and the vehicle drove safely at the intersection.

show abstract

A Low-Cost Consistent Vehicle Localization Based on Interval Constraint Propagation

Cited by 11 publications

References 40 publications

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

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

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

Vision-Based Branch Road Detection for Intersection Navigation in Unstructured Environment Using Multi-Task Network

Contact Info

Product

Resources

About