An Efficient Multi-sensor Fusion Approach for Object Detection in Maritime Environments

Haghbayan, Mohammad-Hashem; Farahnakian, Fahimeh; Poikonen, Jonne; Laurinen, Markus; Nevalainen, Paavo; Plosila, Juha; Heikkonen, Jukka

doi:10.1109/itsc.2018.8569890

Cited by 25 publications

(10 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These two forces are equal in size and opposite in direction [10]. changes occur due to various environments in ships that navigate around the world [25].…”

Section: Rolling Period Of a Shipmentioning

confidence: 99%

“…The types of errors in the MEMS sensor are thermal error, bias error, conversion factor error, and misalignment error, and they can be divided into permanent and temporary errors [33][34][35]. In particular, the temperature is an important factor that is affected by the surrounding environment as in the case of a ship [25]. A temperature change has chemical and physical impacts on the interior of a MEMS sensor [36].…”

Section: Acceleration Errormentioning

confidence: 99%

“…To apply the MEMS inertial sensor to ships, it is necessary to investigate errors that occur during measurements by contemplating the measurement principles of the accelerometer and gyroscope sensor. In particular, thermal errors should be investigated because significant temperature changes occur due to various environments in ships that navigate around the world [25]. Thus, this study aims to analyze the characteristics of thermal errors according to temperature changes in commercial MEMS sensors to apply the six-axis MEMS sensor to MASS.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of Thermal Characteristics of MEMS Sensors for Measuring the Rolling Period of Maritime Autonomous Surface Ships

Lee

Park

2022

JMSE

View full text Add to dashboard Cite

Recently, with the emergence of maritime autonomous surface ships (MASS), ensuring seaworthiness has increased with the operation of MASS. Ship stability is important for safety, and technical methods for controlling a ship’s motion are required to evaluate the stability. A ship’s rolling period is estimated using microelectromechanical systems (MEMS) sensors to measure the ship’s metacentric height. However, weather changes (e.g., temperature) are drastic due to various marine environments in the sea. Hence, it is necessary to analyze MEMS sensors’ thermal characteristics for applying them to MASS. This study aims to analyze the thermal characteristics of a siX-axis MEMS sensor for its application in MASS. The experiments analyzed measurement errors and noise at six steps in the range of 25 °C–75 °C in which the MEMS sensor can be operated. The experimental results showed that the gyroscope’s thermal error and MEMS sensor’s noise level were much larger than those of the accelerometer and the respective thermal error values along the Z-axis of the accelerometer and gyroscope were the most stable compared to those along the other axes. The findings can be applied to a measurement method of the stability of MASS employing MEMS sensors in navigation equipment.

show abstract

“…These two forces are equal in size and opposite in direction [10]. changes occur due to various environments in ships that navigate around the world [25].…”

Section: Rolling Period Of a Shipmentioning

confidence: 99%

Section: Acceleration Errormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of Thermal Characteristics of MEMS Sensors for Measuring the Rolling Period of Maritime Autonomous Surface Ships

Lee

Park

2022

JMSE

View full text Add to dashboard Cite

show abstract

“…Ultimately, a sensor is only considered "smart" when the computer resources is an integral part of the physical sensor design [15]. Invariably, the overall performance of an AV system is greatly enhanced with multiple sensors of different types (smart/non-smart) and modalities (visual, infrared and radio waves) operating at different range and bandwidth (data rate) and with the data of each being incorporated to produce a fused output [16,17,18]. Multi-sensor fusion is effectively now a requisite process in all AD systems, so as to overcome the shortcomings of individual sensor types, improving the efficiency and reliability of the overall AD system.…”

Section: Introductionmentioning

confidence: 99%

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Yeong¹,

Velasco-Hernandez²,

Barry³

et al. 2021

Preprint

135

View full text Add to dashboard Cite

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.

show abstract

“…These include GPS and five basic odometry approaches for GPS-denied navigation, i.e., wheel, inertial, radar, visual, and laser odometries. A combination of different sensors, i.e., multisensor data fusion, is commonly used in object detection and odometry methods to improve the accuracy and robustness of the system [19], [20]. For example, combining inertial and visual odometries leads to a new type of approach called visual-inertial odometry.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Odometry for Autonomous Navigation Systems

et al. 2019

Self Cite

View full text Add to dashboard Cite

The development of a navigation system is one of the major challenges in building a fully autonomous platform. Full autonomy requires a dependable navigation capability not only in a perfect situation with clear GPS signals but also in situations, where the GPS is unreliable. Therefore, selfcontained odometry systems have attracted much attention recently. This paper provides a general and comprehensive overview of the state of the art in the field of self-contained, i.e., GPS denied odometry systems, and identifies the out-coming challenges that demand further research in future. Self-contained odometry methods are categorized into five main types, i.e., wheel, inertial, laser, radar, and visual, where such categorization is based on the type of the sensor data being used for the odometry. Most of the research in the field is focused on analyzing the sensor data exhaustively or partially to extract the vehicle pose. Different combinations and fusions of sensor data in a tightly/loosely coupled manner and with filtering or optimizing fusion method have been investigated. We analyze the advantages and weaknesses of each approach in terms of different evaluation metrics, such as performance, response time, energy efficiency, and accuracy, which can be a useful guideline for researchers and engineers in the field. In the end, some future research challenges in the field are discussed.

show abstract

An Efficient Multi-sensor Fusion Approach for Object Detection in Maritime Environments

Cited by 25 publications

References 8 publications

Analysis of Thermal Characteristics of MEMS Sensors for Measuring the Rolling Period of Maritime Autonomous Surface Ships

Analysis of Thermal Characteristics of MEMS Sensors for Measuring the Rolling Period of Maritime Autonomous Surface Ships

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

A Survey on Odometry for Autonomous Navigation Systems

Contact Info

Product

Resources

About