LiDAR-Stereo Thermal Sensor Fusion for Indoor Disaster Environment

Rho, Sehwan; Park, Seong min; Pyo, Juhyun; Lee, Meungsuk; Jin, Maolin; Yu, Son-Cheol

doi:10.1109/jsen.2023.3245619

Cited by 3 publications

(2 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In response to these challenges, thermal cameras operating in the infrared wavelength range have proven effective in visually degraded environments with varying illumination. Rho et al [116] utilize stereo thermal cameras in conjunction with LiDAR for indoor disaster scenarios. Moreover, radar and event cameras have demonstrated robust performance in challenging environmental conditions.…”

Section: Initial Estimationmentioning

confidence: 99%

LiDAR odometry survey: recent advancements and remaining challenges

Lee,

Jung,

Yang

et al. 2024

Intel Serv Robotics

View full text Add to dashboard Cite

Odometry is crucial for robot navigation, particularly in situations where global positioning methods like global positioning system are unavailable. The main goal of odometry is to predict the robot’s motion and accurately determine its current location. Various sensors, such as wheel encoder, inertial measurement unit (IMU), camera, radar, and Light Detection and Ranging (LiDAR), are used for odometry in robotics. LiDAR, in particular, has gained attention for its ability to provide rich three-dimensional (3D) data and immunity to light variations. This survey aims to examine advancements in LiDAR odometry thoroughly. We start by exploring LiDAR technology and then scrutinize LiDAR odometry works, categorizing them based on their sensor integration approaches. These approaches include methods relying solely on LiDAR, those combining LiDAR with IMU, strategies involving multiple LiDARs, and methods fusing LiDAR with other sensor modalities. In conclusion, we address existing challenges and outline potential future directions in LiDAR odometry. Additionally, we analyze public datasets and evaluation methods for LiDAR odometry. To our knowledge, this survey is the first comprehensive exploration of LiDAR odometry.

show abstract

Section: Initial Estimationmentioning

confidence: 99%

LiDAR odometry survey: recent advancements and remaining challenges

Lee,

Jung,

Yang

et al. 2024

Intel Serv Robotics

View full text Add to dashboard Cite

show abstract

“…Some researchers have successfully generated point clouds of uneven indoor environments with haze by combining LiDAR and stereo thermal infrared cameras. This approach has been used for environmental perception and target detection, yielding promising results (Rho et al. , 2023).…”

Section: Introductionmentioning

confidence: 99%

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

Zhang,

Yuan,

Meng

et al. 2024

View full text Add to dashboard Cite

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.

show abstract