Multi-modal sensor fusion for highly accurate vehicle motion state estimation

Marco, Vicent Rodrigo; Kalkkuhl, Jens; Raisch, Jörg; Scholte, Wouter J.; Nijmeijer, Henk; Seel, Thomas

doi:10.1016/j.conengprac.2020.104409

Cited by 19 publications

(13 citation statements)

References 35 publications

(50 reference statements)

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“…One prime example of a use case where smart sensors that can, besides the measurement data, provide the related uncertainty information while simultaneously enabling secure data exchange is autonomous vehicles [ 62 , 63 ]. The whole concept for safe operation of autonomous vehicles is dependent on data quality, authenticity, and integrity.…”

Section: Use Case Examplesmentioning

confidence: 99%

Metrological Challenges in Collaborative Sensing: Applicability of Digital Calibration Certificates

Mustapää

Nikander

Hutzschenreuter

et al. 2020

Sensors

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IoT systems based on collaborative sensor networks are becoming increasingly common in various industries owing to the increased availability of low-cost sensors. The quality of the data provided by these sensors may be unknown. For these reasons, advanced data processing and sensor network self-calibration methods have become popular research topics. In terms of metrology, the self-calibration methods lack the traceability to the established measurement standards of National Metrology Institutes (NMIs) through an unbroken chain-link of calibration. This problem can be solved by the ongoing digitalization of the metrology infrastructure. We propose a conceptual solution based on Digital Calibration Certificates (DCCs), Digital SI (D-SI), and cryptographic digital identifiers, for validation of data quality and trustworthiness. The data that enable validation and traceability can be used to improve analytics, decision-making, and security in industrial applications. We discuss the applicability and benefits of our solutions in a selection of industrial use cases, where collaborative sensing has already been introduced. We present the remaining challenges in the digitization and standardization processes regarding digital metrology and the future work required to address them.

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Section: Use Case Examplesmentioning

confidence: 99%

Metrological Challenges in Collaborative Sensing: Applicability of Digital Calibration Certificates

Mustapää

Nikander

Hutzschenreuter

et al. 2020

Sensors

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“…The spring deflections, supplied by the vehicle level sensors, together with the tyre deflections, provided by a simple tyre vertical model, are used to compute the vehicle levels over-road z i [3]. Here, i ∈ [FL, FR, RL, RR], where FL, FR, RL, and RR denotes to the front left, front right, rear left and rear right wheels, respectively.…”

Section: Roll and Pitch Misalignmentmentioning

confidence: 99%

“…Hence, nowadays vehicles are equipped with sensors devoted to the estimation of the vehicle motion. In order to meet the high accuracy and reliability demands, multi-modal sensor fusion has become ubiquitous in the field of vehicle motion estimation [1][2][3]. Sensors relying on different technologies, with complementary benefits and deficiencies, are combined in order to increase the overall accuracy and reliability of the motion estimation solution.…”

Section: Introductionmentioning

confidence: 99%

“…Comparatively, the extrinsic calibration of systems not reliant on perception sensors has received considerably less attention. Motion estimation schemes fusing information from odometers (based on wheel speed and steering angle sensors), vehicle models and inertial sensors have been broadly used in active safety and automated driving technologies [3,13,14]. In such a set-up, the IMU angular rates and specific forces are given in the coordinate system of the IMU while the information supplied by the combination of odometers with kinematic [15] and dynamic models [16] is customarily expressed in a vehicle coordinate system.…”

Section: Introductionmentioning

confidence: 99%

“…The misalignment angles remain within the range of [−3, 3] deg and, hence, the smallangle approximation holds (sin α ≈ α, cos α ≈ 1). Keeping in mind that the vehicle and IMU coordinate systems are not aligned, the kinematic differential equations based on the 6D IMU signals are expressed in the IMU coordinate system [3]…”

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confidence: 99%

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A Novel IMU Extrinsic Calibration Method for Mass Production Land Vehicles

Marco

Kalkkuhl

Raisch

et al. 2020

Sensors

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Multi-modal sensor fusion has become ubiquitous in the field of vehicle motion estimation. Achieving a consistent sensor fusion in such a set-up demands the precise knowledge of the misalignments between the coordinate systems in which the different information sources are expressed. In ego-motion estimation, even sub-degree misalignment errors lead to serious performance degradation. The present work addresses the extrinsic calibration of a land vehicle equipped with standard production car sensors and an automotive-grade inertial measurement unit (IMU). Specifically, the article presents a method for the estimation of the misalignment between the IMU and vehicle coordinate systems, while considering the IMU biases. The estimation problem is treated as a joint state and parameter estimation problem, and solved using an adaptive estimator that relies on the IMU measurements, a dynamic single-track model as well as the suspension and odometry systems. Additionally, we show that the validity of the misalignment estimates can be assessed by identifying the misalignment between a high-precision INS/GNSS and the IMU and vehicle coordinate systems. The effectiveness of the proposed calibration procedure is demonstrated using real sensor data. The results show that estimation accuracies below 0.1 degrees can be achieved in spite of moderate variations in the manoeuvre execution.

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