A New Technique for Integrating MEMS-Based Low-Cost IMU and GPS in Vehicular Navigation

Navidi, Néda; Landry, René; Cheng, Jianhua; Gingras, Denis

doi:10.1155/2016/5365983

Cited by 16 publications

(15 citation statements)

References 28 publications

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“…Toth and colleagues applied neural networks and fuzzy logic [ 10 ] to model IMU error sources. Navidi et al [ 11 ] used hybrid Fuzzy Inference Systems (FIS) and the second-order extended Kalman filter to integrate GNSS and IMU sensors. Xing and colleagues showed that chaotic particle swarm optimization significantly reduced gyroscope random drift [ 12 ].…”

Section: Problem Statementmentioning

confidence: 99%

Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study

Hosseinyalamdary

2018

Sensors

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Bayes filters, such as the Kalman and particle filters, have been used in sensor fusion to integrate two sources of information and obtain the best estimate of unknowns. The efficient integration of multiple sensors requires deep knowledge of their error sources. Some sensors, such as Inertial Measurement Unit (IMU), have complicated error sources. Therefore, IMU error modelling and the efficient integration of IMU and Global Navigation Satellite System (GNSS) observations has remained a challenge. In this paper, we developed deep Kalman filter to model and remove IMU errors and, consequently, improve the accuracy of IMU positioning. To achieve this, we added a modelling step to the prediction and update steps of the Kalman filter, so that the IMU error model is learned during integration. The results showed our deep Kalman filter outperformed the conventional Kalman filter and reached a higher level of accuracy.

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Section: Problem Statementmentioning

confidence: 99%

Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study

Hosseinyalamdary

2018

Sensors

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“…Recently, micro-electro-mechanical system (MEMS) gyroscopes have been widely studied and employed in aviation, attitude controlling, and guidance, on account of their excellent precision, fast response, and low cost, among other advantages [1][2][3]. Every coin has two sides, however: the performance of a MEMS gyroscope degrades rapidly with changes in temperature, which can limit its application [4].…”

Section: Introductionmentioning

confidence: 99%

Dual-Mass MEMS Gyroscope Parallel Denoising and Temperature Compensation Processing Based on WLMP and CS-SVR

2020

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For the sake of decreasing the effects of noise and temperature error on the measurement accuracy of micro-electro-mechanical system (MEMS) gyroscopes, a denoising and temperature drift compensation parallel model method based on wavelet transform and forward linear prediction (WFLP) and support vector regression based on the cuckoo search algorithm (CS-SVR) is proposed in this paper. First, variational mode decomposition (VMD) is proposed in this paper, which is aimed at dividing the output signal of the gyroscope into intrinsic mode functions (IMFs); then, the IMFs are classified into three features—drift, mixed, and pure noise features—by the sample entropy (SE) value. Second, a wavelet transform and forward linear prediction (WFLP) are combined to remove the noise from the mixed features. Meanwhile, the drift feature is compensated by support vector regression based on the cuckoo search algorithm (CS-SVR). Finally, through reconstruction, the final signal is obtained. Experimental results demonstrate that the VMD-SE-WFLP-CS-SVR method proposed in this paper can decrease noise and compensate the temperature error effectively (angular random walking value is optimized from 1.667°/√h to 0.0667°/√h and the bias stability is reduced from 30°/h to 4°/h). In terms of denoising, the performance of the WFLP algorithm is superior to the wavelet threshold and FLP, as it combines their advantages; furthermore, in terms of temperature compensation, the proposed CS-SVR algorithm uses the cuckoo search algorithm to find the optimal parameters of SVR, improving the accuracy of the model.

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“…To solve this task, an autonomous transport machine must be equipped with a set of sensors that allow it to accurately determine its position relative to the surrounding limits. Currently, the most commonly used navigation systems for agricultural machines are the Global Navigation Satellite System (GNSS), Machine Vision Navigation System, Light Detection and Ranging (LIDAR), and Combined Navigation Systems composed of two or more subsystems [2][3][4][5][6][7]. The most affordable sensor for direct measurement of position, GNSS, does not reach this level of accuracy [8].…”

Section: Introductionmentioning

confidence: 99%

3D Autonomous Navigation Line Extraction for Field Roads Based on Binocular Vision

Wang

Liu³

2019

Journal of Sensors

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This paper proposes a 3D autonomous navigation line extraction method for field roads in hilly regions based on a low-cost binocular vision system. Accurate guide path detection of field roads is a prerequisite for the automatic driving of agricultural machines. First, considering the lack of lane lines, blurred boundaries, and complex surroundings of field roads in hilly regions, a modified image processing method was established to strengthen shadow identification and information fusion to better distinguish the road area from its surroundings. Second, based on nonobvious shape characteristics and small differences in the gray values of the field roads inside the image, the centroid points of the road area as its statistical feature was extracted and smoothed and then used as the geometric primitives of stereo matching. Finally, an epipolar constraint and a homography matrix were applied for accurate matching and 3D reconstruction to obtain the autonomous navigation line of the field roads. Experiments on the automatic driving of a carrier on field roads showed that on straight roads, multicurvature complex roads and undulating roads, the mean deviations between the actual midline of the road and the automatically traveled trajectory were 0.031 m, 0.069 m, and 0.105 m, respectively, with maximum deviations of 0.133, 0.195 m, and 0.216 m, respectively. These test results demonstrate that the proposed method is feasible for road identification and 3D navigation line acquisition.

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A New Technique for Integrating MEMS-Based Low-Cost IMU and GPS in Vehicular Navigation

Cited by 16 publications

References 28 publications

Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study

Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; A GNSS/IMU Case Study

Dual-Mass MEMS Gyroscope Parallel Denoising and Temperature Compensation Processing Based on WLMP and CS-SVR

3D Autonomous Navigation Line Extraction for Field Roads Based on Binocular Vision

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