Calibration and improvements of the high-resolution range-imaging camera SwissRanger

Kahlmann, Timo; Ingensand, Hilmar

doi:10.1117/12.582513

Cited by 21 publications

(20 citation statements)

References 4 publications

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“…In the same manner, multiple FPN are calcu- LUT IT = compute 3-degree polynomial look-up table; 7: FPN = compute fixed pattern noise; 8: LUT A = compute amplitude look-up table; 9: else 10: N i = choose integration time set; 11: for all i such that 1 ≤ i ≤ size(N i ) do 12: LUT i = compute B-Spline look-up table for N i ; 13: FPN i = compute fixed pattern noise for N i ; 14: end for 15: end if lated for each IT, while short-range applications need only one. Systematic amplitude errors due to object reflectivity can be considered negligible for large range scenes if compared to the other influencing factors [54,53].…”

Section: Depth Calibrationmentioning

confidence: 99%

ToF cameras for active vision in robotics

Alenyà

Foix

Torras

2014

Sensors and Actuators A: Physical

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ToF cameras are now a mature technology that is widely being adopted to provide sensory input to robotic applications. Depending on the nature of the objects to be perceived and the viewing distance, we distinguish two groups of applications: those requiring to capture the whole scene and those centered on an object. It will be demonstrated that it is in this last group of applications, in which the robot has to locate and possibly manipulate an object, where the distinctive characteristics of ToF cameras can be better exploited.After presenting the physical sensor features and the calibration requirements of such cameras, we review some representative works highlighting for each one which of the distinctive ToF characteristics have been more essential. Even if at low resolution, the acquisition of 3D images at frame-rate is one of the most important features, as it enables quick background/foreground segmentation. A common use is in combination with classical color cameras. We present three developed applications, using a mobile robot and a robotic arm, to exemplify with real images some of the stated advantages.

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Section: Depth Calibrationmentioning

confidence: 99%

ToF cameras for active vision in robotics

Alenyà

Foix

Torras

2014

Sensors and Actuators A: Physical

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“…These effects can cause systematic errors that are significantly greater than the measurement precision, and thus need to be calibrated or compensated for. Kahlmann et al 2,6 discuss some of these error sources.…”

Section: Introductionmentioning

confidence: 99%

Modelling and compensating measurement errors caused by scattering in time-of-flight cameras

et al. 2008

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Recently, Range Imaging (RIM) cameras have become available that capture high resolution range images at video rate. Such cameras measure the distance from the scene for each pixel independently based upon a measured time of flight (TOF). Some cameras, such as the SwissRanger™ SR-3000, measure the TOF based on the phase shift of reflected light from a modulated light source. Such cameras are shown to be susceptible to severe distortions in the measured range due to light scattering within the lens and camera. Earlier work induced using a simplified Gaussian point spread function and inverse filtering to compensate for such distortions. In this work a method is proposed for how to identify and use generally shaped empirical models for the point spread function to get a more accurate compensation. The otherwise difficult inverse problem is solved by using the forward model iteratively, according to well established procedures from image restoration. Each iteration is done as a sequential process, starting with the brightest parts of the image and then moving sequentially to the least bright parts, with each step subtracting the estimated effects from the measurements. This approach gives a faster and more reliable compensation convergence. An average reduction of the error by more than 60% is demonstrated on real images. The computation load corresponds to one or two convolutions of the measured complex image with a real filter of the same size as the image.

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“…Ingensand 2005 andWestfeld et al 2009). The quality of single distance measurements clearly varies with ambient light conditions (Nitsche et al 2010).…”

Section: Rimmentioning

confidence: 99%

Processing of Uav Based Range Imaging Data to Generate Detailed Elevation Models of Complex Natural Structures

Kohoutek

Eisenbeiss

2012

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

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Commission I, WG I/V KEY WORDS: RIM, UAV, tracking, differential GNSS, mapping, DEM/DTM ABSTRACT:Unmanned Aerial Vehicles (UAVs) are more and more used in civil areas like geomatics. Autonomous navigated platforms have a great flexibility in flying and manoeuvring in complex environments to collect remote sensing data. In contrast to standard technologies such as aerial manned platforms (airplanes and helicopters) UAVs are able to fly closer to the object and in small-scale areas of high-risk situations such as landslides, volcano and earthquake areas and floodplains. Thus, UAVs are sometimes the only practical alternative in areas where access is difficult and where no manned aircraft is available or even no flight permission is given. Furthermore, compared to terrestrial platforms, UAVs are not limited to specific view directions and could overcome occlusions from trees, houses and terrain structures. Equipped with image sensors and/or laser scanners they are able to provide elevation models, rectified images, textured 3D-models and maps. In this paper we will describe a UAV platform, which can carry a range imaging (RIM) camera including power supply and data storage for the detailed mapping and monitoring of complex structures, such as alpine riverbed areas. The UAV platform NEO from Swiss UAV was equipped with the RIM camera CamCube 2.0 by PMD Technologies GmbH to capture the surface structures. Its navigation system includes an autopilot. To validate the UAV-trajectory a 360° prism was installed and tracked by a total station. Within the paper a workflow for the processing of UAV-RIM data is proposed, which is based on the processing of differential GNSS data in combination with the acquired range images. Subsequently, the obtained results for the trajectory are compared and verified with a track of a UAV (Falcon 8, Ascending Technologies) carried out with a total station simultaneously to the GNSS data acquisition. The results showed that the UAV's position using differential GNSS could be determined in the centimetre to the decimetre level. The RIM data indicated a high noise level in the measured distance image, due to the vibrations caused by the flight system. Multi-image processing reduced the noise level of the distance image. The produced elevation models from a test area show the high potential of the proposed method for complex structures such as riverbeds.

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Calibration and improvements of the high-resolution range-imaging camera SwissRanger

Cited by 21 publications

References 4 publications

ToF cameras for active vision in robotics

ToF cameras for active vision in robotics

Modelling and compensating measurement errors caused by scattering in time-of-flight cameras

Processing of Uav Based Range Imaging Data to Generate Detailed Elevation Models of Complex Natural Structures

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