Neuro-evolutionary mobile robot egomotion estimation with a 3D ToF camera

Villaverde, Iván; Graña, Manuel

doi:10.1007/s00521-010-0384-6

Cited by 13 publications

(6 citation statements)

References 14 publications

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“…TOF cameras as a developing type of 3D vision sensor are attracting more and more attentions for autonomous mobile robotics [42], [57], [201]. The real-time 3D data can help robots to accomplish autonomous path-planning [57], [202]. Therefore, the vision system now becomes indispensable for the robots to see the environment and avoid possible obstacles, e.g.…”

Section: Real-time Data Processing In Dynamic Scenementioning

confidence: 99%

Recent Advances in 3D Data Acquisition and Processing by Time-of-Flight Camera

Chen

2019

IEEE Access

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Three-dimensional (3D) data acquisition and real-time processing is a critical issue in an artificial vision system. The developing time-of-flight (TOF) camera as a real-time vision sensor for obtaining depth images has now received wide attention, due to its great potential in many areas, such as 3D perception, computer vision, robot navigation, human-machine interaction, augmented reality, and so on. This paper survey advances in TOF imaging technology mainly from the last decade. We focus only on recent progress of overcoming limitations such as systematic errors, object boundary ambiguity, multipath error, phase wrapping, and motion blur, and address the theoretical principles and future research trends as well. INDEX TERMS Vision sensor, time of flight, depth image, sensing device, computer vision, 3D vision.

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Section: Real-time Data Processing In Dynamic Scenementioning

confidence: 99%

Recent Advances in 3D Data Acquisition and Processing by Time-of-Flight Camera

Chen

2019

IEEE Access

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“…There are hundreds of articles published in prestigious scientific magazines stating successful applications of ANNs that have been used for about seventy years, especially in the electrics, electronics, chemistry, production, robotics, material sciences [21][22][23], the economy [24], physical metallurgy, automotive, defense and telecommunication [25] areas.…”

Section: Annsmentioning

confidence: 99%

Approximate and Three-Dimensional Modeling of Brightness Levels in Interior Spaces by Using Artificial Neural Networks

Şahin¹,

Oğuz²,

Büyüktümtürk³

2015

Journal of Electrical Engineering and Technology

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-In this study, artificial neural networks were used to determine the intensity of brightness in interior spaces. The illumination elements to illuminate indoor spaces were considered, not individually, but as a system. So, during the planned maintenance periods of an illumination system, after its design and installation, simple brightness level measurements must be taken. For a threedimensional evaluation of the brightness level in indoor spaces in a speedy and accurate manner, the obtained brightness level measurement results and artificial neural network model were used. Upon estimation of the most suitable brightness level for indoor spaces by using the artificial neutral network model, the energy demands required by the illumination elements decreased. Consequently, in this study, with estimations of brightness levels, the extent to which the artificial neutral networks become successful was observed and more correct results have been obtained in terms of both economy and usage.

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“…Villaverde and Graña [27] proposed an algorithm for ego-motion estimation using artificial neural networks. Their algorithm has three stages.…”

Section: Tof Camera Ego-motion Estimationmentioning

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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Neuro-evolutionary mobile robot egomotion estimation with a 3D ToF camera

Cited by 13 publications

References 14 publications

Recent Advances in 3D Data Acquisition and Processing by Time-of-Flight Camera

Recent Advances in 3D Data Acquisition and Processing by Time-of-Flight Camera

Approximate and Three-Dimensional Modeling of Brightness Levels in Interior Spaces by Using Artificial Neural Networks

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

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