Sensors for 3D Imaging: Metric Evaluation and Calibration of a CCD/CMOS Time-of-Flight Camera

Chiabrando, Filiberto; Chiabrando, Roberto; Piatti, Dario; Rinaudo, Fulvio

doi:10.3390/s91210080

Cited by 131 publications

(98 citation statements)

References 17 publications

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“…The accuracy of ToF cameras varies depending on the internal components and the characteristics of the observed scene, such as objects reflectivity and ambient lighting conditions. These errors cannot be fully eliminated, but they can be reduced and optimized thanks to filtering or several techniques, such us averaging techniques or calibration procedures Chiabrando et al (2009) where the proposed distance error model provided a reduction of distance errors in the 1.5-4m distance measurement range.…”

Section: Data Acquisitionmentioning

confidence: 99%

Using GNG to improve 3D feature extraction—Application to 6DoF egomotion

et al. 2012

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Several recent works deal with 3D data in mobile robotic problems, e.g. mapping or egomotion. Data comes from any kind of sensor such as stereo vision systems, time of flight cameras or 3D lasers, providing a huge amount of unorganized 3D data. In this paper, we describe an efficient method to build complete 3D models from a Growing Neural Gas (GNG). The GNG is applied to the 3D raw data and it reduces both the subjacent error and the number of points, keeping the topology of the 3D data. The GNG output is then used in a 3D features extraction method. We have performed a deep study in which we quantitatively show that the use of GNG improves the 3D feature extraction method. We also show that our method can be applied to any kind of 3D data. The 3D features obtained are used as input in an Iterative Closest Point (ICP)-like method to compute the 6DoF movement performed by a mobile robot. A comparison with standard ICP is performed, showing that the use of GNG improves the results. Final results of 3D mapping from the egomotion calculated are also shown.

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Section: Data Acquisitionmentioning

confidence: 99%

Using GNG to improve 3D feature extraction—Application to 6DoF egomotion

et al. 2012

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“…Depending upon the exact method of signal generation, it is quite plausible for the entire reference waveform to change shape as the temperature changes. Global phase shifts have been noted in measurements produced by a SwissRanger 4000 [29]. This has the potential to make aliasing calibration substantially more difficult, unless using approaches such as harmonic cancellation.…”

Section: Spatiotemporal Variation In the Correlation Waveformmentioning

confidence: 99%

Understanding and Ameliorating Non-Linear Phase and Amplitude Responses in AMCW Lidar

2011

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Amplitude modulated continuous wave (AMCW) lidar systems commonly suffer from non-linear phase and amplitude responses due to a number of known factors such as aliasing and multipath inteference. In order to produce useful range and intensity information it is necessary to remove these perturbations from the measurements. We review the known causes of non-linearity, namely aliasing, temporal variation in correlation waveform shape and mixed pixels/multipath inteference. We also introduce other sources of non-linearity, including crosstalk, modulation waveform envelope decay and non-circularly symmetric noise statistics, that have been ignored in the literature. An experimental study is conducted to evaluate techniques for mitigation of non-linearity, and it is found that harmonic cancellation provides a significant improvement in phase and amplitude linearity.

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“…The room temperature was maintained constant (20 °C) for all the tests and the distance measurements were analyzed for two hours of camera operation in each test. This procedure was already proposed in [23], but here the analytical calculations are explained in more detail and the results for both cameras are reported. Room lights were switched off during the tests in order to avoid influence on the camera measurements.…”

Section: Warm-up Period Evaluationmentioning

confidence: 99%

“…However, this doesn't affect the results of the test as only relative variations of the distance measurements are considered in the following. Suitable accuracy tests have already been performed during other experiments [23] for the SR-4000 camera and will be performed for the CamCube3.0 too in the future. Figure 9.…”

Section: Test On the Sr-4000 Cameramentioning

confidence: 99%

SR-4000 and CamCube3.0 Time of Flight (ToF) Cameras: Tests and Comparison

Piatti

Rinaudo

2012

Remote Sensing

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Abstract:In this paper experimental comparisons between two Time-of-Flight (ToF) cameras are reported in order to test their performance and to give some procedures for testing data delivered by this kind of technology. In particular, the SR-4000 camera by Mesa Imaging AG and the CamCube3.0 by PMD Technologies have been evaluated since they have good performances and are well known to researchers dealing with Time-ofFlight (ToF) cameras. After a brief overview of commercial ToF cameras available on the market and the main specifications of the tested devices, two topics are presented in this paper. First, the influence of camera warm-up on distance measurement is analyzed: a warm-up of 40 minutes is suggested to obtain the measurement stability, especially in the case of the CamCube3.0 camera, that exhibits distance measurement variations of several centimeters. Secondly, the variation of distance measurement precision variation over integration time is presented: distance measurement precisions of some millimeters are obtained in both cases. Finally, a comparison between the two cameras based on the experiments and some information about future work on evaluation of sunlight influence on distance measurements are reported.

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Sensors for 3D Imaging: Metric Evaluation and Calibration of a CCD/CMOS Time-of-Flight Camera

Cited by 131 publications

References 17 publications

Using GNG to improve 3D feature extraction—Application to 6DoF egomotion

Using GNG to improve 3D feature extraction—Application to 6DoF egomotion

Understanding and Ameliorating Non-Linear Phase and Amplitude Responses in AMCW Lidar

SR-4000 and CamCube3.0 Time of Flight (ToF) Cameras: Tests and Comparison

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