Lock-in Time-of-Flight (ToF) Cameras: A Survey

Foix, Salvador Cardona; Alenyà, Guillem; Torras, Carme

doi:10.1109/jsen.2010.2101060

Cited by 548 publications

(341 citation statements)

References 44 publications

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“…Two types of errors, systematic and non-systematic, can interfere and consequently corrupt ToF depth readings. A detailed ToF error description and classification can be found in [2].…”

Section: Depth Measurement Errors and Calibrationmentioning

confidence: 99%

“…These are false points that appear between the edges of the objects and the background. These points can be easily located in the depth image and the 3D point cloud, and easy-to-implement filtering methods are available [2]. …”

Section: Non-systematic Errorsmentioning

confidence: 99%

“…Regarding the quality of data, it is well known that raw ToF data is quite noisy and prone to several types of disturbances [2]. Some of them are systematic and can be calibrated, and others are non-systematic and sometimes can be filtered out.…”

Section: Introductionmentioning

confidence: 99%

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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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“…Two types of errors, systematic and non-systematic, can interfere and consequently corrupt ToF depth readings. A detailed ToF error description and classification can be found in [2].…”

Section: Depth Measurement Errors and Calibrationmentioning

confidence: 99%

Section: Non-systematic Errorsmentioning

confidence: 99%

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ToF cameras for active vision in robotics

Alenyà

Foix

Torras

2014

Sensors and Actuators A: Physical

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“…Both cases are negligible in our modelling task, because the desired object is always at the nearest distance in the sensor's field of view and consequently the most illuminated. For a more detailed and broad classification and explanation of the different error sources, advantages and limitations of 3D ToF cameras, please refer to [5].…”

Section: D Tof Cameramentioning

confidence: 99%

Information-Gain View Planning for Free-Form Object Reconstruction with a 3D ToF Camera

Foix

Kriegel

Fuchs

et al. 2012

Advanced Concepts for Intelligent Vision Systems

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Abstract. Active view planning for gathering data from an unexplored 3D complex scenario is a hard and still open problem in the computer vision community. In this paper, we present a general task-oriented approach based on an information-gain maximization that easily deals with such a problem. Our approach consists of ranking a given set of possible actions, based on their taskrelated gains, and then executing the best-ranked action to move the required sensor. An example of how our approach behaves is demonstrated by applying it over 3D raw data for real-time volume modelling of complex-shaped objects. Our setting includes a calibrated 3D time-of-flight (ToF) camera mounted on a 7 degrees of freedom (DoF) robotic arm. Noise in the sensor data acquisition, which is too often ignored, is here explicitly taken into account by computing an uncertainty matrix for each point, and refining this matrix each time the point is seen again. Results show that, by always choosing the most informative view, a complete model of a 3D free-form object is acquired and also that our method achieves a good compromise between speed and precision.

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“…These cameras are also not suitable computationally for path planning in a 3D environment. In order to enhance the vision capabilities of the robot to interact effectively with human, Time-of-Flight (ToF) depth sensor is used because of its many advantages [7], [8] such as they are not affected by the intensity differences between images and provide immediate depth information of the environment. ToF sensors seems to be an effective solution, which rapidly perceive the 3D environment and are less affected by light conditions.…”

Section: Introductionmentioning

confidence: 99%

Human-Robot Collaboration: Twofold Strategy Algorithm to Avoid Collisions Using ToF Sensor

Plapper¹

2015

IJMMM

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Abstract-The importance of Human Robot Interaction to complement human skills in a manufacturing environment with industrial robots increases the concerns over safety of human and the robot. It is necessary to identify collision risks and avoid them otherwise production stops may cost a huge amount to the industry. A robot working at manufacturing facility should be able to predict potential collisions and must be able to prevent i.e. react automatically for safe detour around obstacle/human. Currently, industrial robots are able to detect collisions after a real contact but the existing proposals for avoiding collisions are either computationally expensive or not very well adapted to human safety. The objective of this paper is to provide intelligence to the industrial robot to predict collision risks and react automatically without stopping the production in a static environment. The proposed approach using Time of Flight (TOF) camera, provides decision regarding trajectory correction and improvement by shifting robot to a secure position. The application presented in this paper is for safe KUKA robot trajectory generation in peg-in-hole assembly process in the laboratory context. Index Terms-Robot path planning, safe robot trajectory generation, collision detection and avoidance, vision based system, time of flight (ToF) sensor.

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Lock-in Time-of-Flight (ToF) Cameras: A Survey

Cited by 548 publications

References 44 publications

ToF cameras for active vision in robotics

ToF cameras for active vision in robotics

Information-Gain View Planning for Free-Form Object Reconstruction with a 3D ToF Camera

Human-Robot Collaboration: Twofold Strategy Algorithm to Avoid Collisions Using ToF Sensor

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