Visual navigation for indoor mobile robots using a single camera

Dao, Nguyen Xuan; You, Bum-Jae; Oh, Sang–Rok

doi:10.1109/iros.2005.1545494

Cited by 9 publications

(2 citation statements)

References 12 publications

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“…Image processing techniques have also been of interest for detecting and recognizing guide paths during the development of AGVs [4][5][6][7][8][9][10]. In many studies related to vision-based orientation, various methods have been introduced for use in navigation systems, e.g., a feedback optimal controller [11], a fuzzy logic controller [12], neural networks, and interactive learning navigation.…”

Section: Introductionmentioning

confidence: 99%

Vision Sensor-Based Driving Algorithm for Indoor Automatic Guided Vehicles

Van¹,

Eum

Lee³

et al. 2013

International Journal of Fuzzy Logic and Intelligent Systems

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In this paper, we describe a vision sensor-based driving algorithm for indoor automatic guided vehicles (AGVs) that facilitates a path tracking task using two mono cameras for navigation. One camera is mounted on vehicle to observe the environment and to detect markers in front of the vehicle. The other camera is attached so the view is perpendicular to the floor, which compensates for the distance between the wheels and markers. The angle and distance from the center of the two wheels to the center of marker are also obtained using these two cameras. We propose five movement patterns for AGVs to guarantee smooth performance during path tracking: starting, moving straight, pre-turning, left/right turning, and stopping. This driving algorithm based on two vision sensors gives greater flexibility to AGVs, including easy layout change, autonomy, and even economy. The algorithm was validated in an experiment using a two-wheeled mobile robot.

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Section: Introductionmentioning

confidence: 99%

Vision Sensor-Based Driving Algorithm for Indoor Automatic Guided Vehicles

Van¹,

Eum

Lee³

et al. 2013

International Journal of Fuzzy Logic and Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…Concerning visual navigation, many reactive and deliberative navigation approaches have been presented up to now, e.g., in structured environments using white line recognition [44], in corridor navigation using View-Sequenced Route Representation [45], or more complex techniques combining visual localization with the extraction of valid planar region [46], or visual and navigation techniques to perform visual navigation and obstacle avoidance [47]. Some works integrate and fuse vision data from the UAV and UGV for best target tracking [48], another work [49] presents uncertainty modeling and observation-fusion approaches that produce considerable improvement in geo-location accuracy.…”

Section: Related Workmentioning

confidence: 99%

Tracking and intercepting pedestrians: a robotic approach to surveillance of critical infrastructures

Oviedo¹

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A single landmark based localization algorithm for non-holonomic mobile robots

Sert

Kokosy

Perruquetti

2011

2011 IEEE International Conference on Robotics and Automation

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This paper proposes a single landmark based localization algorithm for non-holonomic mobile robots. In the case of a unicycle robot model, the localization problem is equivalent to the system observability. Based on this observation, the proposed localization method consists in finding a vector function which depends on the measurement vector and its derivatives. In order to compute estimates of the successive derivatives of the measurement vector, we will use a numerical differentiation method. When the robot is able to only measure the relative angle between itself and the landmark in 2D case, the algorithm estimates the posture of the robot, under the hypothesis that control inputs are known. But, sometimes it is also useful to be able to estimate the control input (for example when the robot slips). This is possible with the proposed algorithm by using a landmark in dimension three. The simulation results will be given in order to show the effectiveness of the proposed algorithm. Moreover, these results are compared with those obtained by an Extended Kalman Filter in order to underline the advantages of the new algorithm.

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Visual navigation for indoor mobile robots using a single camera

Cited by 9 publications

References 12 publications

Vision Sensor-Based Driving Algorithm for Indoor Automatic Guided Vehicles

Vision Sensor-Based Driving Algorithm for Indoor Automatic Guided Vehicles

Tracking and intercepting pedestrians: a robotic approach to surveillance of critical infrastructures

A single landmark based localization algorithm for non-holonomic mobile robots

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