Vision-based robot localization across seasons and in remote locations

Viswanathan, Anirudh; Pires, Bernardo R.; Huber, Daniel

doi:10.1109/icra.2016.7487685

Cited by 26 publications

(15 citation statements)

References 18 publications

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“…However, their proposed approach needs apparent surface information of the road. Reference [87] designed a coarsegrained semantics (e.g., flat terrain, shrubs, or tree) to match with a ground image and a satellite map and achieved reasonable vision-based localisation performance across seasons. Reference [88] combined a semi-dense image description based on a histogram of oriented gradient features and global descriptors from deep convolutional neural networks trained on ImageNet for image matching.…”

Section: Vision-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: Vision-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

“…Radio effects such as very few satellites or multipath propagation will cause the degradation of position estimation. Conte and Doherty [9] and Viswanathan et al [10] believe that GNSS jammers will cause signal interference, making position estimation unreliable. UAVs rely only on GNSS signals that may be maliciously interfered with, which will seriously affect mission execution and even cause catastrophic consequences.…”

Section: Introductionmentioning

confidence: 99%

UAV Pose Estimation in GNSS-Denied Environment Assisted by Satellite Imagery Deep Learning Features

Hou

Lan

et al. 2021

IEEE Access

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“…In recent years, research on autonomous navigation for urban environments has been garnering increasing attention. Many publications have targeted different aspects of navigation such as route planning, perception and localization [1,2,3,4]. For trustworthy operation in each of these aspects, assessing the level of safety of the vehicle from potential system failures is critical.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Protection Levels for Camera and 3D Map-based Safe Urban Localization

Gupta¹,

Gao²

2021

Preprint

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Reliably assessing the error in an estimated vehicle position is integral for ensuring the vehicle's safety in urban environments. Many existing approaches use GNSS measurements to characterize protection levels (PLs) as probabilistic upper bounds on the position error. However, GNSS signals might be reflected or blocked in urban environments, and thus additional sensor modalities need to be considered to determine PLs. In this paper, we propose a novel approach for computing PLs by matching camera image measurements to a LiDAR-based 3D map of the environment. We specify a Gaussian mixture model probability distribution of position error using deep neural network-based data-driven models and statistical outlier weighting techniques. From the probability distribution, we compute the PLs by evaluating the position error bound using numerical line-search methods. Through experimental validation with real-world data, we demonstrate that the PLs computed from our method are reliable bounds on the position error in urban environments.Recently, data-driven techniques based on deep neural networks (DNNs) have demonstrated state-of-the-art performance in determining the state of the camera sensor, comprising of its position and orientation, by identifying and matching

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Vision-based robot localization across seasons and in remote locations

Cited by 26 publications

References 18 publications

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

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

UAV Pose Estimation in GNSS-Denied Environment Assisted by Satellite Imagery Deep Learning Features

Data-Driven Protection Levels for Camera and 3D Map-based Safe Urban Localization

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