Mobile robot localization using odometry and kinect sensor

Ganganath, Nuwan; Leung, Henry

doi:10.1109/espa.2012.6152453

Cited by 73 publications

(33 citation statements)

References 5 publications

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“…[10] discusses the feasibility and design behind a simple robotic skeleton which utilizes the Kinect to mimic human movements in real-time. [8] presents a mobile robot localization system for an indoor environment using a Kinect sensor; b) image processing which is being used for the detection and recognition of gestures, which is essential for the man-machine interaction [11]- [16]. In [12] a method to recognize gestures through the Kinect depth camera is proposed, the depth image is used for background removal, followed by generation of the depth profile of the subject.…”

Section: Introductionmentioning

confidence: 99%

Bilateral Virtual Control Human-Machine with Kinect Sensor

Andaluz

Gallardo

Santana

et al. 2012

2012 VI Andean Region International Conference

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This work presents the virtual bilateral interaction between Man-Machine through the Kinect sensor, allowing the user to control and monitor Windows programs by gestures without the need of using peripheral components which means that "You Are The Control". In order to develop this work the body is tracked, which lets the user to manipulate the computer depending on their needs. In this application two functions of the Kinect sensor are used: the depth camera and the Skeleton Tracking, which is why object oriented programming on Visual Studio 2010 (C#, WPF) was used. Additionally, several experimental tests were carried out, through which the performance of the proposal was verified.

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Section: Introductionmentioning

confidence: 99%

Bilateral Virtual Control Human-Machine with Kinect Sensor

Andaluz

Gallardo

Santana

et al. 2012

2012 VI Andean Region International Conference

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“…The detection of a human presence is achieved in Xia, Chen, and Aggarwal (2011), where the depth infor mation and 2D information is associated to the contour of the head. Moreover, the Kinect device can also be used as a comple mentary sensor in a complex system (Ganganath & Leung, 2012), where a mobile robot uses a Kinect device for the location of land marks to correct the robot's position. Despite Kinect is not time of flight sensor, it is used for 3D environment mapping, that is, there are others and different applications that have been developed, such as Soutschek, Penne, Hornegger, and Kornhuber (2008), so a touch less user interface is developed for certain medical applica tions where sterility requirements in the operation's room prevent direct contact.…”

Section: Driver Safety Through Facial Recognitionmentioning

confidence: 99%

IVVI 2.0: An intelligent vehicle based on computational perception

Martín

García

Musleh

et al. 2014

Expert Systems with Applications

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This is a postprint version of the following published document: Martín, D., et al. (2014) a b s t r a c tThis paper presents the IVVI 2.0 a smart research platform to foster intelligent systems in vehicles. Com putational perception in intelligent transportation systems applications has advantages, such as huge data from vehicle environment, among others, so computer vision systems and laser scanners are the main devices that accomplish this task. Both have been integrated in our intelligent vehicle to develop cutting edge applications to cope with perception difficulties, data processing algorithms, expert knowl edge, and decision making. The long term in vehicle applications, that are presented in this paper, out perform the most significant and fundamental technical limitations, such as, robustness in the face of changing environmental conditions. Our intelligent vehicle operates outdoors with pedestrians and oth ers vehicles, and outperforms illumination variation, i.e.: shadows, low lighting conditions, night vision, among others. So, our applications ensure the suitable robustness and safety in case of a large variety of lighting conditions and complex perception tasks. Some of these complex tasks are overcome by the improvement of other devices, such as, inertial measurement units or differential global positioning sys tems, or perception architectures that accomplish sensor fusion processes in an efficient and safe manner. Both extra devices and architectures enhance the accuracy of computational perception and outreach the properties of each device separately.

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“…Unlike in indoor applications in which terrains are assumed to be flat [9]- [11], the energy consumption of mobile robots navigating on uneven terrains heavily depends on their routes. Therefore, our goal is to determine energy-efficient routes for these mobile robots.…”

Section: Introductionmentioning

confidence: 99%

Finding energy-efficient paths on uneven terrains

Ganganath

Cheng

Tse

2014

2014 10th France-Japan/ 8th Europe-Asia Congress on Mecatronics (MECATRONICS2014- Tokyo)

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Abstract-Mobile robots are increasingly getting popular in outdoor applications. Long period of continuous operations are common in such applications. Therefore, robot motions need to be optimized to minimize their energy consumption. Shortest paths do not always guarantee minimum energy consumptions of mobile robots. This paper proposes a novel algorithm to generate energyefficient paths on uneven terrains using an established energycost model for mobile robots. Terrains are represented using grid based elevation maps. Similar to A* algorithm, the energy-cost of traversing through a particular gird depends on a heuristic energy-cost estimation from the current location to the goal. The proposed heuristic energy-cost function makes it possible to generate zigzag-like path patterns on steep hills under the power limitations of the robot. Therefore, the proposed method can find physically feasible energy-efficient paths on any given terrain, provided that such paths exist. Simulation results presented in this paper demonstrate the performance of the proposed algorithm on uneven terrains maps.

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Mobile robot localization using odometry and kinect sensor

Cited by 73 publications

References 5 publications

Bilateral Virtual Control Human-Machine with Kinect Sensor

Bilateral Virtual Control Human-Machine with Kinect Sensor

IVVI 2.0: An intelligent vehicle based on computational perception

Finding energy-efficient paths on uneven terrains

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