Extrinsic calibration method for integrating infrared thermal imaging camera and 3D LiDAR

IF 1.6 4区 工程技术 Q3 INSTRUMENTS & INSTRUMENTATION
Dan Zhang, Junji Yuan, Haibin Meng, Wei Wang, Rui He, Sen Li
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引用次数: 0

Abstract

Purpose

In the context of fire incidents within buildings, efficient scene perception by firefighting robots is particularly crucial. Although individual sensors can provide specific types of data, achieving deep data correlation among multiple sensors poses challenges. To address this issue, this study aims to explore a fusion approach integrating thermal imaging cameras and LiDAR sensors to enhance the perception capabilities of firefighting robots in fire environments.

Design/methodology/approach

Prior to sensor fusion, accurate calibration of the sensors is essential. This paper proposes an extrinsic calibration method based on rigid body transformation. The collected data is optimized using the Ceres optimization algorithm to obtain precise calibration parameters. Building upon this calibration, a sensor fusion method based on coordinate projection transformation is proposed, enabling real-time mapping between images and point clouds. In addition, the effectiveness of the proposed fusion device data collection is validated in experimental smoke-filled fire environments.

Findings

The average reprojection error obtained by the extrinsic calibration method based on rigid body transformation is 1.02 pixels, indicating good accuracy. The fused data combines the advantages of thermal imaging cameras and LiDAR, overcoming the limitations of individual sensors.

Originality/value

This paper introduces an extrinsic calibration method based on rigid body transformation, along with a sensor fusion approach based on coordinate projection transformation. The effectiveness of this fusion strategy is validated in simulated fire environments.

集成红外热像仪和 3D 激光雷达的外部校准方法
目的 在建筑物内发生火灾事故时,消防机器人对现场的高效感知尤为重要。虽然单个传感器可以提供特定类型的数据,但要在多个传感器之间实现深度数据关联则是一项挑战。为解决这一问题,本研究旨在探索一种融合热成像摄像机和激光雷达传感器的方法,以增强消防机器人在火灾环境中的感知能力。本文提出了一种基于刚体转换的外部校准方法。使用 Ceres 优化算法对收集到的数据进行优化,以获得精确的校准参数。在此校准的基础上,提出了一种基于坐标投影变换的传感器融合方法,实现了图像和点云之间的实时映射。此外,还在实验性烟雾弥漫的火灾环境中验证了所提出的融合设备数据收集的有效性。研究结果基于刚体变换的外校准方法获得的平均重投影误差为 1.02 像素,表明精度良好。融合数据结合了红外热像仪和激光雷达的优势,克服了单个传感器的局限性。 原创性/价值 本文介绍了一种基于刚体变换的外校准方法,以及一种基于坐标投影变换的传感器融合方法。这种融合策略的有效性在模拟火灾环境中得到了验证。
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来源期刊
Sensor Review
Sensor Review 工程技术-仪器仪表
CiteScore
3.40
自引率
6.20%
发文量
50
审稿时长
3.7 months
期刊介绍: Sensor Review publishes peer reviewed state-of-the-art articles and specially commissioned technology reviews. Each issue of this multidisciplinary journal includes high quality original content covering all aspects of sensors and their applications, and reflecting the most interesting and strategically important research and development activities from around the world. Because of this, readers can stay at the very forefront of high technology sensor developments. Emphasis is placed on detailed independent regular and review articles identifying the full range of sensors currently available for specific applications, as well as highlighting those areas of technology showing great potential for the future. The journal encourages authors to consider the practical and social implications of their articles. All articles undergo a rigorous double-blind peer review process which involves an initial assessment of suitability of an article for the journal followed by sending it to, at least two reviewers in the field if deemed suitable. Sensor Review’s coverage includes, but is not restricted to: Mechanical sensors – position, displacement, proximity, velocity, acceleration, vibration, force, torque, pressure, and flow sensors Electric and magnetic sensors – resistance, inductive, capacitive, piezoelectric, eddy-current, electromagnetic, photoelectric, and thermoelectric sensors Temperature sensors, infrared sensors, humidity sensors Optical, electro-optical and fibre-optic sensors and systems, photonic sensors Biosensors, wearable and implantable sensors and systems, immunosensors Gas and chemical sensors and systems, polymer sensors Acoustic and ultrasonic sensors Haptic sensors and devices Smart and intelligent sensors and systems Nanosensors, NEMS, MEMS, and BioMEMS Quantum sensors Sensor systems: sensor data fusion, signals, processing and interfacing, signal conditioning.
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