A robust sketch interface for natural robot control

Shah, Danelle C.; Schneider, J.; Campbell, Mark

doi:10.1109/iros.2010.5649345

Cited by 19 publications

(8 citation statements)

References 10 publications

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“…The system computes a path in the hand-drawn map, and the robot execute it in an open-loop controller, without localizing the robot. Shah et al [18] propose a similar approach. The focus of the work, however, is on how to extract qualitative instructions for the robot.…”

Section: Related Workmentioning

confidence: 99%

Monte Carlo localization in hand-drawn maps

Behzadian

Agarwal

Burgard

et al. 2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Robot localization is a one of the most important problems in robotics. Most of the existing approaches assume that the map of the environment is available beforehand and focus on accurate metrical localization. In this paper, we address the localization problem when the map of the environment is not present beforehand, and the robot relies on a handdrawn map from a non-expert user. We addressed this problem by expressing the robot pose in the pixel coordinate and simultaneously estimate a local deformation of the hand-drawn map. Experiments show that we are able to localize the robot in the correct room with a robustness up to 80%.

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Section: Related Workmentioning

confidence: 99%

Monte Carlo localization in hand-drawn maps

Behzadian

Agarwal

Burgard

et al. 2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…Gesture recognition techniques have been recently used to develop several human-robot interaction systems with a natural and intelligent interface. Some of these systems, [SHAH, D. et al 2012], [YANG, H.D. et al 2007], [GONZALEZ-SANCHEZ, T. & PUIG, D. 2011], [SERAFIMOV, K. et al 2012] use HMM or its variations, although some other recognition techniques have also been used, such as localist actuator network (LAN) [YAN, R. et al 2012] and fuzzy associative memory (FAM) [SATO, E. et al 2007].…”

Section: Advances In Distributed Computing and Artificial Intelligence Jornualmentioning

confidence: 99%

User-independent accelerometer-based gesture recognition for mobile devices

Wang

Tarrío

Bernardos

et al. 2013

ADCAIJ

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Many mobile devices embed nowadays inertial sensors. This enables new forms of human-computer interaction through the use of gestures (movements performed with the mobile device) as a way of communication. This paper presents an accelerometer-based gesture recognition system for mobile devices which is able to recognize a collection of 10 different hand gestures. The system was conceived to be light and to operate in a user-independent manner in real time. The recognition system was implemented in a smart phone and evaluated through a collection of user tests, which showed a recognition accuracy similar to other state-of-the art techniques and a lower computational complexity. The system was also used to build a human-robot interface that enables controlling a wheeled robot with the gestures made with the mobile phone

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“…11 Figure 1 gives a pictorial overview of the architecture. Key elements of the architecture as as follows:…”

Section: System Level Overviewmentioning

confidence: 99%

Distributed, collaborative human-robotic networks for outdoor experiments in search, identify and track

et al. 2010

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This paper presents an overview of a human-robotic system under development at Cornell which is capable of mapping an unknown environment, as well as discovering, tracking, and neutralizing several static and dynamic objects of interest. In addition, the robots can coordinate their individual tasks with one another without overly burdening a human operator. The testbed utilizes the Segway RMP platform, with lidar, vision, IMU and GPS sensors. The software draws from autonomous systems research, specifically in the areas of pose estimation, target detection and tracking, motion and behavioral planning, and human robot interaction. This paper also details experimental scenarios of mapping, tracking, and neutralization presented by way of pictures, data, and movies.

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A robust sketch interface for natural robot control

Cited by 19 publications

References 10 publications

Monte Carlo localization in hand-drawn maps

Monte Carlo localization in hand-drawn maps

User-independent accelerometer-based gesture recognition for mobile devices

Distributed, collaborative human-robotic networks for outdoor experiments in search, identify and track

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