Enhanced Map-Aided GPS/3D RISS Combined Positioning Strategy in Urban Canyons

Song, Xiang; Ren, Chunxiao; Jiang, Huilin; Li, Liping; Wu, Wei; Li, Ling; Yan, Shun; Zhang, Bingyu; Wu, Jiaen

doi:10.1155/2022/7650435

Cited by 4 publications

(2 citation statements)

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“…The states are propagated using ( 3)-( 4), and the covariance is propagated using (9). The G n matrix is a 9 × 9 identity matrix, and the F n is defined as follows:…”

Section: A Radar Inertial Dead-recknoning Systemmentioning

confidence: 99%

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A Novel Method for Land Vehicle Positioning: Invariant Kalman Filters and Deep-Learning-Based Radar Velocity Estimation

Araujo¹,

Elhabiby²,

Givigi³

et al. 2023

Preprint

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<p>Autonomous and intelligent vehicles are multi-sensor systems operating in various environments and conditions. Due to their characteristics, inertial measurement units (IMUs) are typically the core component of such systems. However, these sensors rapidly accumulate errors due to biases and noise, degrading the positioning solution. Therefore, this paper presents a positioning solution that only uses three gyroscopes and one radar. The proposed method was tested using low-cost sensors in different scenarios, such as open-sky, urban and indoor areas. The key components of the method are the invariant Kalman filters and the use of deep neural networks to estimate the forward velocity of the car using the radar readings. The method was tested on a custom dataset, and our integrated solution accurately estimates the vehicle's 3D position, velocity, and orientation. We achieved, on average, a 1.45% translational error in the tested scenarios, making the proposed method a robust alternative to current IMU-based positioning methods.</p>

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“…The states are propagated using ( 3)-( 4), and the covariance is propagated using (9). The G n matrix is a 9 × 9 identity matrix, and the F n is defined as follows:…”

Section: A Radar Inertial Dead-recknoning Systemmentioning

confidence: 99%

“…It uses a reduced set of sensors and an odometer to estimate the state of the vehicle more accurately. Recent publications have achieved compelling results with this method [5]- [9]. However, RISS has limitations.…”

Section: Introductionmentioning

confidence: 99%