Online IMU Self-Calibration for Visual-Inertial Systems

Xiao, Yao; Ruan, Xiaogang; Chai, Jianping; Zhang, Xiaoping; Zhu, Xijing

doi:10.3390/s19071624

Cited by 29 publications

(13 citation statements)

References 51 publications

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“…Algorithms and methods for intrinsic calibration of sensors have received considerable attention with significant advancement over the last number of years and now, are well-established in the literature. These algorithms and methodologies may vary from one sensor to another [ 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 ]. This subsection aims to provide an overview of the most used calibration targets and the calibration methodologies for the pinhole camera model.…”

Section: Sensor Calibration and Sensor Fusion For Object Detectionmentioning

confidence: 99%

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Yeong

Velasco-Hernandez

Barry

et al. 2021

Sensors

490

133

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With the significant advancement of sensor and communication technology and the reliable application of obstacle detection techniques and algorithms, automated driving is becoming a pivotal technology that can revolutionize the future of transportation and mobility. Sensors are fundamental to the perception of vehicle surroundings in an automated driving system, and the use and performance of multiple integrated sensors can directly determine the safety and feasibility of automated driving vehicles. Sensor calibration is the foundation block of any autonomous system and its constituent sensors and must be performed correctly before sensor fusion and obstacle detection processes may be implemented. This paper evaluates the capabilities and the technical performance of sensors which are commonly employed in autonomous vehicles, primarily focusing on a large selection of vision cameras, LiDAR sensors, and radar sensors and the various conditions in which such sensors may operate in practice. We present an overview of the three primary categories of sensor calibration and review existing open-source calibration packages for multi-sensor calibration and their compatibility with numerous commercial sensors. We also summarize the three main approaches to sensor fusion and review current state-of-the-art multi-sensor fusion techniques and algorithms for object detection in autonomous driving applications. The current paper, therefore, provides an end-to-end review of the hardware and software methods required for sensor fusion object detection. We conclude by highlighting some of the challenges in the sensor fusion field and propose possible future research directions for automated driving systems.

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Section: Sensor Calibration and Sensor Fusion For Object Detectionmentioning

confidence: 99%

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Yeong

Velasco-Hernandez

Barry

et al. 2021

Sensors

490

133

View full text Add to dashboard Cite

show abstract

“…Algorithms and methods for intrinsic calibration of sensors have received considerable attention with significant advancement over the last number of years and now, are well-established in the literature. These algorithms and methodologies may vary from one sensor to another [120][121][122][123][124][125][126][127]. This subsection aims to provide an overview of the most commonly used calibration targets and the calibration methodologies for the pinhole camera model.…”

Section: Intrinsic Calibration Overviewmentioning

confidence: 99%

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Yeong¹,

Velasco-Hernandez²,

Barry³

et al. 2021

Preprint

149

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The market for autonomous vehicles (AV) is expected to experience significant growth over the coming decades and to revolutionize the future of transportation and mobility. The AV is a vehicle that is capable of perceiving its environment and perform driving tasks safely and efficiently with little or no human intervention and is anticipated to eventually replace conventional vehicles. Self-driving vehicles employ various sensors to sense and perceive their surroundings and, also rely on advances in 5G communication technology to achieve this objective. Sensors are fundamental to the perception of surroundings and the development of sensor technologies associated with AVs has advanced at a significant pace in recent years. Despite remarkable advancements, sensors can still fail to operate as required, due to for example, hardware defects, noise and environment conditions. Hence, it is not desirable to rely on a single sensor for any autonomous driving task. The practical approaches shown in recent research is to incorporate multiple, complementary sensors to overcome the shortcomings of individual sensors operating independently. This article reviews the technical performance and capabilities of sensors applicable to autonomous vehicles, mainly focusing on vision cameras, LiDAR and Radar sensors. The review also considers the compatibility of sensors with various software systems enabling the multi-sensor fusion approach for obstacle detection. This review article concludes by highlighting some of the challenges and possible future research directions.

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“…Generally, engineered devices are subject to unavoidable imperfections due to technical limitations of manufacturing processes and material properties. e defects result in many uncertainties (i.e., noise and disturbance) affecting the device's operation [15,16]. Device developers need to invest in more time and effort to understand the sources and impacts of these uncertainties before proposing any algorithm and designing optimal software architecture.…”

Section: Related Workmentioning

confidence: 99%

CKF-Based Visual Inertial Odometry for Long-Term Trajectory Operations

Nguyen

Mann

Vardy

et al. 2020

Journal of Robotics

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The estimation error accumulation in the conventional visual inertial odometry (VIO) generally forbids accurate long-term operations. Some advanced techniques such as global pose graph optimization and loop closure demand relatively high computation and processing time to execute the optimization procedure for the entire trajectory and may not be feasible to be implemented in a low-cost robotic platform. In an attempt to allow the VIO to operate for a longer duration without either using or generating a map, this paper develops iterated cubature Kalman filter for VIO application that performs multiple corrections on a single measurement to optimize the current filter state and covariance during the measurement update. The optimization process is terminated using the maximum likelihood estimate based criteria. For comparison, this paper also develops a second solution to integrate VIO estimation with ranging measurements. The wireless communications between the vehicle and multiple beacons produce the ranging measurements and help to bound the accumulative errors. Experiments utilize publicly available dataset for validation, and a rigorous comparison between the two solutions is presented to determine the application scenario of each solution.

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Online IMU Self-Calibration for Visual-Inertial Systems

Cited by 29 publications

References 51 publications

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

CKF-Based Visual Inertial Odometry for Long-Term Trajectory Operations

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