Bias-Error Accumulation Analysis for Inertial Navigation Methods

Liu, Xinyu; Zhou, Qingfeng; Chen, Xiang; Fan, Lisheng; Cheng, Chi‐Tsun

doi:10.1109/lsp.2021.3129151

Cited by 9 publications

(2 citation statements)

References 17 publications

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“…The main problem with relying solely on the IMU for the navigation and localization operations is the complex environmental conditions in which the AV has to operate. When primarily relying on the IMU, the navigation error is rapidly accumulated [31], which ultimately leads to the AV's disorientation. In this case, such an error can be corrected by the GPS coordinates; however, the GPS system might be unavailable for a considerable time.…”

Section: Integration Of the Computer-vision-based Localization Into T...mentioning

confidence: 99%

Robust Autonomous Vehicle Computer-Vision-Based Localization in Challenging Environmental Conditions

et al. 2023

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In this paper, we present a novel autonomous vehicle (AV) localization design and its implementation, which we recommend to employ in challenging navigation conditions with a poor quality of the satellite navigation system signals and computer vision images. In the case when the GPS signal becomes unstable, other auxiliary navigation systems, such as computer-vision-based positioning, are employed for more accurate localization and mapping. However, the quality of data obtained from AV’s sensors might be deteriorated by the extreme environmental conditions too, which infinitely leads to the decrease in navigation performance. To verify our computer-vision-based localization system design, we considered the Arctic region use case, which poses additional challenges for the AV’s navigation and might employ artificial visual landmarks for improving the localization quality, which we used for the computer vision training. We further enhanced our data by applying affine transformations to increase its diversity. We selected YOLOv4 image detection architecture for our system design, as it demonstrated the highest performance in our experiments. For the computational platform, we employed a Nvidia Jetson AGX Xavier device, as it is well known and widely used in robotic and AV computer vision, as well as deep learning applications. Our empirical study showed that the proposed computer vision system that was further trained on the dataset enhanced by affine transformations became robust regarding image quality degradation caused by extreme environmental conditions. It was effectively able to detect and recognize images of artificial visual landmarks captured in the extreme Arctic region’s conditions. The developed system can be integrated into vehicle navigation facilities to improve their effectiveness and efficiency and to prevent possible navigation performance deterioration.

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Section: Integration Of the Computer-vision-based Localization Into T...mentioning

confidence: 99%

Robust Autonomous Vehicle Computer-Vision-Based Localization in Challenging Environmental Conditions

et al. 2023

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“…IMU sensors, on the other hand, are immune to those problems and remain available in all settings. The shortcoming of IMUs for localization is that existing integration methods accumulate error rapidly over time [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Learning Car Speed Using Inertial Sensors

Freydin,

2022

Preprint

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A deep neural network (DNN) is trained to estimate the speed of a car driving in an urban area using as input a stream of measurements from a low-cost six-axis inertial measurement unit (IMU). Three hours of data was collected by driving through the city of Ashdod, Israel in a car equipped with a global navigation satellite system (GNSS) real time kinematic (RTK) positioning device and a synchronized IMU. Ground truth labels for the car speed were calculated using the position measurements obtained at the high rate of 50 [Hz]. A DNN architecture with long short-term memory layers is proposed to enable high-frequency speed estimation that accounts for previous inputs history and the nonlinear relation between speed, acceleration and angular velocity. A simplified aided dead reckoning localization scheme is formulated to assess the trained model which provides the speed pseudo-measurement. The trained model is shown to substantially improve the position accuracy during a 4 minutes drive without the use of GNSS position updates.

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