Robust low resolution thermal stereo camera calibration

Zoetgnande, Yannick Wend Kuni; Fougères, Alain-Jérôme; Cormier, Geoffroy; Dillenseger, Jean-Louis

doi:10.1117/12.2523440

Cited by 13 publications

(11 citation statements)

References 18 publications

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“…There are two main strategies to build a target usable for thermal infrared camera calibration: Active targets which require partial heating or cooling for the generation of contrast have been employed for such calibrations before (Rankin et al, 2011), (Vidas et al, 2012), (Ursine et al, 2012), (Yu et al, 2013), (Zoetgnande et al, 2018), (Chen et al, 2019). Reflective targets using different emissivity surfaces are also possible, but require different temperatures for the target itself and an illumination source (Shibata et al (2017) and Aguilera et al (2018)).…”

Section: Related Workmentioning

confidence: 99%

Automatic Calibration and Co-Registration for a Stereo Camera System and a Thermal Imaging Sensor Using a Chessboard

Choinowski

Dahlke

Ernst

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> This paper presents a system calibration method for a trifocal sensor, which is sensitive to different spectral bands. The trifocal camera system consists of a stereo camera, operating in the visual (VIS) spectrum and a thermal imaging camera, operating in the Long-Wave- Infrared (LWIR) spectrum. Intrinsic parameters and spatial alignment are determined simultaneously. As calibration target a passive aluminium chessboard is used. Corner detection and subsequent bundle adjustment is done on all synchronized image triplets. The remaining reprojection errors are in the sub-pixel range and enable the system to generate metric point clouds, colored with thermal intensities in real-time.</p>

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Section: Related Workmentioning

confidence: 99%

Automatic Calibration and Co-Registration for a Stereo Camera System and a Thermal Imaging Sensor Using a Chessboard

Choinowski

Dahlke

Ernst

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Zoetgnande et al propose an active method to calibrate a low-cost low-resolution stereo LWIR thermal camera system. They design a board with 36 bulbs and develop a sub-pixel corner estimation algorithm to detect these heat signatures against a wooden calibration frame [28]. Zalud et al detail a five camera RGB-D(ToF)-T system for robotic telepresence [29].…”

Section: B Calibrationmentioning

confidence: 99%

PST900: RGB-Thermal Calibration, Dataset and Segmentation Network

Shivakumar¹,

Rodrigues²,

Zhou³

et al. 2019

Preprint

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In this work we propose long wave infrared (LWIR) imagery as a viable supporting modality for semantic segmentation using learning-based techniques. We first address the problem of RGB-thermal camera calibration by proposing a passive calibration target and procedure that is both portable and easy to use. Second, we present PST900, a dataset of 894 synchronized and calibrated RGB and Thermal image pairs with per pixel human annotations across four distinct classes from the DARPA Subterranean Challenge. Lastly, we propose a CNN architecture for fast semantic segmentation that combines both RGB and Thermal imagery in a way that leverages RGB imagery independently. We compare our method against the state-of-the-art and show that our method outperforms them in our dataset.• A method of RGB and LWIR (thermal) camera calibra-

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“…Other active methods depend on heated elements, such as light bulbs (Yang et al [33], Zoetgnand et al [37]), resistors (Gschwandtner et al [13]) or overheated LEDs (Beauvisage and Aouf [2], Li et al [19]).…”

Section: Existing Lwir and Multimodal Calibrationmentioning

confidence: 99%

Long Range 3D with Quadocular Thermal (LWIR) Camera

Filippov,

Dzhimiev

2019

Preprint

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Long Wave Infrared (LWIR) cameras provide images regardless of the ambient illumination, they tolerate fog and are not blinded by the incoming car headlights. These features make LWIR cameras attractive for autonomous navigation, security and military applications. Thermal images can be used similarly to the visible range ones, including 3D scene reconstruction with two or more such cameras mounted on a rigid frame. There are two additional challenges for this spectral range: lower image resolution and lower contrast of the textures.In this work, we demonstrate quadocular LWIR camera setup, calibration, image capturing and processing that result in long range 3D perception with 0.077 pix disparity error over 90% of the depth map. With low resolution (160 × 120) LWIR sensors we achieved 10% range accuracy at 28 m with 56 • horizontal field of view (HFoV) and 150 mm baseline. Scaled to the now-standard 640 × 512 resolution and 200 mm baseline suitable for head-mounted application the result would be 10% accuracy at 130 m.

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Robust low resolution thermal stereo camera calibration

Cited by 13 publications

References 18 publications

Automatic Calibration and Co-Registration for a Stereo Camera System and a Thermal Imaging Sensor Using a Chessboard

Automatic Calibration and Co-Registration for a Stereo Camera System and a Thermal Imaging Sensor Using a Chessboard

PST900: RGB-Thermal Calibration, Dataset and Segmentation Network

Long Range 3D with Quadocular Thermal (LWIR) Camera

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