Integrating Vision Based Navigation with INS and GPS for Land Vehicle Navigation in Challenging GNSS Environments

Sun, Yunlong; Rahman, Muhammed Tahsin; Karamat, Tashfeen B.; Noureldin, Aboelmagd; Gao, Yanbin

doi:10.33012/2016.14561

Cited by 7 publications

(3 citation statements)

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“…is first denoised. The azimuth angle, however, requires an integration, and since land vehicle navigation is sensitive to azimuth angle errors [41], VO is used to suppress the resulting drift. Therefore, in the error state formulation, only the error in azimuth angle and the gyroscope bias are considered.…”

Section: Integration Filtermentioning

confidence: 99%

Improving Multisensor Positioning of Land Vehicles with Integrated Visual Odometry for Next-Generation Self-Driving Cars

Rahman

Karamat

Givigi

et al. 2018

Journal of Advanced Transportation

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For their complete realization, autonomous vehicles (AVs) fundamentally rely on the Global Navigation Satellite System (GNSS) to provide positioning and navigation information. However, in area such as urban cores, parking lots, and under dense foliage, which are all commonly frequented by AVs, GNSS signals suffer from blockage, interference, and multipath. These effects cause high levels of errors and long durations of service discontinuity that mar the performance of current systems. The prevalence of vision and low-cost inertial sensors provides an attractive opportunity to further increase the positioning and navigation accuracy in such GNSS-challenged environments. This paper presents enhancements to existing multisensor integration systems utilizing the inertial navigation system (INS) to aid in Visual Odometry (VO) outlier feature rejection. A scheme called Aided Visual Odometry (AVO) is developed and integrated with a high performance mechanization architecture utilizing vehicle motion and orientation sensors. The resulting solution exhibits improved state covariance convergence and navigation accuracy, while reducing computational complexity. Experimental verification of the proposed solution is illustrated through three real road trajectories, over two different land vehicles, and using two low-cost inertial measurement units (IMUs).

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Section: Integration Filtermentioning

confidence: 99%

Improving Multisensor Positioning of Land Vehicles with Integrated Visual Odometry for Next-Generation Self-Driving Cars

Rahman

Karamat

Givigi

et al. 2018

Journal of Advanced Transportation

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“…The feature points are extracted and matched to the acquired image sequence, and the matched feature points are then utilized to solve the transformation relationship of different frame images, and so the motion parameters are estimated. It is also possible to set up the landmarks [6] and measure their information in advance, and then obtain the vehicular position through the vehicle matching landmarks to achieve the purpose of precisely navigation. In the literature, some works that focus on the INS [7][8][9] and vision had been done.…”

Section: Introductionmentioning

confidence: 99%

Vehicular Navigation Based on the Fusion of 3D-RISS and Machine Learning Enhanced Visual Data in Challenging Environments

Sun

Guan

et al. 2020

Electronics

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Based on the 3D Reduced Inertial Sensor System (3D-RISS) and the Machine Learning Enhanced Visual Data (MLEVD), an integrated vehicle navigation system is proposed in this paper. In demanding conditions such as outdoor satellite signal interference and indoor navigation, this work incorporates vehicle smooth navigation. Firstly, a landmark is set up and both of its size and position are accurately measured. Secondly, the image with the landmark information is captured quickly by using the machine learning. Thirdly, the template matching method and the Extended Kalman Filter (EKF) are then used to correct the errors of the Inertial Navigation System (INS), which employs the 3D-RISS to reduce the overall cost and ensuring the vehicular positioning accuracy simultaneously. Finally, both outdoor and indoor experiments are conducted to verify the performance of the 3D-RISS/MLEVD integrated navigation technology. Results reveal that the proposed method can effectively reduce the accumulated error of the INS with time while maintaining the positioning error within a few meters.

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“…They are not suitable for wide-ranging applications, especially for private vehicles. Visual positioning is too complicated to calculate for a car computer and too susceptible to dim light in the garage environment [7], and it is not easy to implement in current car navigation systems. Wireless positioning requires a base station (BS) that arranges a signal in the underground area, because the positioning signal is easily blocked by obstacles such as walls, causing positioning errors in narrow space [8].…”

Section: Introductionmentioning

confidence: 99%

A Low-Cost Underground Garage Navigation Switching Algorithm Based on Kalman Filtering

Gao

Wang

Liu

et al. 2019

Sensors

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Modern parking lots have gradually developed into underground garages to improve the efficient use of space. However, the complex design of parking lots also increases the demands on vehicle navigation. The traditional method of navigation switching only uses satellite signals. After the Position Dilution Of Precision (PDOP) of satellite signals is over the limit, vehicle navigation will enter indoor mode. It is not suitable for vehicles in underground garages to switch modes with a fast-response system. Therefore, this paper chooses satellite navigation, inertial navigation, and the car system to combine navigation. With the condition that the vehicle can freely travel through indoor and outdoor environments, high-precision outdoor environment navigation is used to provide the initial state of underground navigation. The position of the vehicle underground is calculated by the Dead Reckoning (DR) navigation system. This paper takes advantage of the Extended Kalman Filter (EKF) algorithm to provide two freely switchable navigation modes for vehicles in ground and underground garages. The continuity, robustness, fast response, and low cost of the indoor and outdoor switching navigation methods are verified in real-time systems.

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Integrating Vision Based Navigation with INS and GPS for Land Vehicle Navigation in Challenging GNSS Environments

Cited by 7 publications

References 0 publications

Improving Multisensor Positioning of Land Vehicles with Integrated Visual Odometry for Next-Generation Self-Driving Cars

Improving Multisensor Positioning of Land Vehicles with Integrated Visual Odometry for Next-Generation Self-Driving Cars

Vehicular Navigation Based on the Fusion of 3D-RISS and Machine Learning Enhanced Visual Data in Challenging Environments

A Low-Cost Underground Garage Navigation Switching Algorithm Based on Kalman Filtering

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