Sensor Fusion Tests for an Autonomous Vehicle, using Extended Kalman Filter

O., Garcia Bedoya; V, Ferreira J.

doi:10.25103/jestr.113.01

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…However, the approaches followed in the literature identify 2D intensity patterns or features from a set of image sequence. The selection was usually made at identifying the patterns and features that are typical to the obstacles including vehicle shadows, their bounding edges and dark wheels [15]. Figure 3b illustrates the detection of lower edge of vehicle shadow by the fusion of cacmra and LiDAR viewing angles using extended Kalman Filter.…”

Section: Resultsmentioning

confidence: 99%

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Hybrid Multi-Sensor Integration for Static or Dynamic Obstacle Detection, Tracking and Classification for Autonomous Vehicle

Rajkumar¹,

Teklemariam²,

Mekonnen³

2021

JUSST

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Autonomous Vehicles (AV) reduces human intervention by perceiving the vehicle’s location with respect to the environment. In this regard, utilization of multiple sensors corresponding to various features of environment perception yields not only detection but also enables tracking and classification of the object leading to high security and reliability. Therefore, we propose to deploy hybrid multi-sensors such as Radar, LiDAR, and camera sensors. However, the data acquired with these hybrid sensors overlaps with the wide viewing angles of the individual sensors, and hence convolutional neural network and Kalman Filter (KF) based data fusion framework was implemented with a goal to facilitate a robust object detection system to avoid collisions inroads. The complete system tested over 1000 road scenarios for real-time environment perception showed that our hardware and software configurations outperformed numerous other conventional systems. Hence, this system could potentially find its application in object detection, tracking, and classification in a real-time environment.

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Section: Resultsmentioning

confidence: 99%

“…The position and direction of the vehicle along with the road geometries can be identifies by inertial sensors and accurate mapping. Inertial sensors determine the continuous position and orientation [15].…”

Section: Inertial Measuring Unit (Imu)mentioning

confidence: 99%

Hybrid Multi-Sensor Integration for Static or Dynamic Obstacle Detection, Tracking and Classification for Autonomous Vehicle

Rajkumar¹,

Teklemariam²,

Mekonnen³

2021

JUSST

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“…The Kalman filter used by the Apollo 11 mission [42] assisted in the guidance of the spacecraft as it approached the moon. Whilst a mainstay of many industries, in particular for navigation [43][44][45] and autonomous vehicles [46], the Kalman filter has seen a surge in application demand as multiple sensors are increasingly adapted for everyday tasks, e.g. signal processing [47].…”

Section: Sensor Fusion Of Rotational Measurementsmentioning

confidence: 99%

Tracking bridge tilt behaviour using sensor fusion techniques

Faulkner

Wang

Huseynov

2020

J Civil Struct Health Monit

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The resilience of the built environment to extreme weather events is fundamental for the day-today operation of our transport network, with scour representing one of the biggest threats to bridges built over flowing water. Condition monitoring of the bridge using a structural health monitoring system enhances resilience by reducing the time needed to return the bridge to normal use by providing timely information on structural condition and safety. The work presented in this report discusses use of rotational measurements in structural health monitoring. Traditionally tiltmeters (which can be a form of DC accelerometer) are used to measure rotation but are known to be affected by dynamic movements, while gyroscopes react quickly to dynamic motion but drift over time. This review will introduce gyroscopes as a complementary sensor for accelerometer rotational measurements and use sensor fusion techniques to combine the measurements from both sensors to get an optimised rotational result. This method was trialled on a laboratory scaled model, before the system was installed on an in-service single-span skewed railway bridge. The rotational measurements were compared against rotation measurements obtained using a vision-based measurement system to confirm the validity of the results. An introduction to gyroscopes, field test measurement results with the sensors and their correlation with the vision-based measurement results are presented in this article.

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