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

Piatti, Dario; Rinaudo, Fulvio

doi:10.3390/rs4041069

Cited by 57 publications

(32 citation statements)

References 13 publications

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“…However compared to other depth sensors, ToF cameras cannot be considered yet as a mature sensor. The disadvantages are obvious such as low resolution, motion artifacts, edge distortion, ambient light noise, et al, [1,5]. Despite its disadvantages, it is already showing great potential in many applications where fast 3D depth data is needed, such as pose estimation [6,7], obstacle avoidance [8,9] and others.…”

Section: Introductionmentioning

confidence: 99%

Depth Map Enhancement based on Improved Adaptive Total Generalized Variation for ToF Camera

Liu¹,

Zhao²,

Bo³

2017

IJSIP

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Section: Introductionmentioning

confidence: 99%

Depth Map Enhancement based on Improved Adaptive Total Generalized Variation for ToF Camera

Liu¹,

Zhao²,

Bo³

2017

IJSIP

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“…Our experiments used the Swiss Ranger ToF cameras. Even though both cameras use the same principle to measure distance, their sensors tend to have different characteristics, based on the number and arrangement of the modulated NIR light sources [8]. The accuracy of these sensors is limited by errors that are caused by the measurement principle, the architecture of the sensors, and the environment (background light and reflectivity of objects).…”

Section: Tof Camera Ego-motion Estimationmentioning

confidence: 99%

“…This is the fourth-generation ToF camera from Swiss Ranger, and most of the systematic errors -such as internal scattering, which was an issue in the third-generation cameras -have been eliminated [13]. The SR4000 does not have temperature compensation, so a 40-minute warm-up time is allowed before any experiments are performed, as suggested in [8]. The non-systematic errors, which are mostly dependent on the scene configuration, are handled by the various filters discussed below.…”

Section: D Tof Pre-processesmentioning

confidence: 99%

An Investigation Into Trajectory Estimation In Underground Mining Environments Using A Time-Of-Flight Camera And An Inertial Measurement Unit

Ratshidaho

Tapamo²,

Claassens³

et al. 2014

The South African Journal of Industrial Engineering

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One of the most important and challenging tasks for mobile robots that navigate autonomously is localisation -the process whereby a robot locates itself within a map of a known environment or with respect to a known starting point within an unknown environment. Localisation of a robot in an unknown environment is done by tracking the trajectory of the robot on the basis of the initial pose. Trajectory estimation becomes a challenge if the robot is operating in an unknown environment that has a scarcity of landmarks, is GPS-denied, has very little or no illumination, and is slippery -such as in underground mines. This paper attempts to solve the problem of estimating a robot's trajectory in underground mining environments using a time-of-flight (ToF) camera and an inertial measurement unit (IMU). In the past, this problem has been addressed by using a 3D laser scanner; but these are expensive and consume a lot of power, even though they have high measurement accuracy and a wide field of view. Here, trajectory estimation is accomplished by the fusion of ego-motion provided by the ToF camera with measurement data provided by a low cost IMU. The fusion is performed using the Kalman filter algorithm on a mobile robot moving on a 2D planar surface. The results show a significant improvement on the trajectory estimation. A Vicon system is used to provide groundtruth for the trajectory estimation. Trajectory estimation only using the ToF camera is prone to errors, especially when the robot is rotating; but the fused trajectory estimation algorithm is able to estimate accurate ego-motion even when the robot is rotating. OPSOMMINGEen van die belangrikste en uitdagendste take vir mobiele robotte om selfstandig te kan navigeer is lokalisering. Lokalisering van 'n robot in 'n onbekende omgewing word gedoen deur die volg van die trajek van die robot (die aanvanklike posisie moet bekend wees). Trajekskatting raak uitdagend as die robot moet funksioneer in 'n onbekende omgewing met 'n tekort aan landmerke, geen GPS opvangs, baie swak of geen verligting en 'n gladde oppervlak -soos in ondergrondse myne. Hierdie artikel poog om die probleem van die skatting van 'n robot se trajek in ondergrondse mynbou omgewing met 'n tyd-van-vlug kamera en traagheid meet eenheid op te los. In die verlede is hierdie probleem aangespreek deur die gebruik van 'n 3D laserskandeerder. 3D laserskandeerders is duur en gebruik baie krag, al is hulle baie akkuraat met 'n wye veld van sig. In hierdie artikel is trajek skatting gedoen deur die samesmelting van die ego-beweging, gekry van die TVV kamera, en die meting data voorsien deur 'n goedkoop TME. Die samesmelting is uitgevoer deur gebruik te maak van die Kalmanfilter algoritme op 'n mobiele robot wat in 'n 2D plat vlak beweeg. Die resultate toon 'n verbetering op die trajekskatting. 'n Vicon stelsel word gebruik om die begin posisie te verskaf vir die trajekskatting. Trajekskatting slegs met die behulp van die TVV kamera is geneig tot foute, veral wanneer die robot draai. Die trajekskatting a...

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“…In addition, the same study carried out a few millimetre differences between the distances measured by total station and the camera. The warm-up time of the camera was investigated on Piatti and Rinaudo (2012). Jamtsho (2010) was investigated accuracy of the ToF camera, and measured the structural deformation with 0.3mm accuracy.…”

Section: Time-of-flight Cameramentioning

confidence: 99%

Measurement and Analysis of Gait by Using a Time-of-Flight Camera

Altuntas¹,

Turkmen²,

Ucar³

et al. 2016

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

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ABSTRACT:Biomedical applications generally needs measurement the human body parts in motion. On the other hand, the analysis of the human motion includes mobile measurements. The mobile measurement is complicated task because it needs two or more sensor combination, specific measurement techniques and huge computation. Thus, it is actual research topic in photogrammetry and computer sciences community. Time-of-flight (ToF) camera can make measurement the moving object. It can be used for robotic and simultaneous localization and mapping applications. Human motion capture is recent application area for ToF camera. In this study analysis of the body motion were made with time-of-flight camera. We made measurement to runner on treadmill. The motion was analysed with computing the angle between body parts.

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

Cited by 57 publications

References 13 publications

Depth Map Enhancement based on Improved Adaptive Total Generalized Variation for ToF Camera

Depth Map Enhancement based on Improved Adaptive Total Generalized Variation for ToF Camera

An Investigation Into Trajectory Estimation In Underground Mining Environments Using A Time-Of-Flight Camera And An Inertial Measurement Unit

Measurement and Analysis of Gait by Using a Time-of-Flight Camera

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