Stable Vision System for Indoor Moving Robot Using Encoder Information

Shim, Eun Sub; Hwang, Wonsang; Anjum, Muhammad Latif; Park, Kwang Suk; Kim, Kwangsoo; Cho, Dong‐il Dan

doi:10.3182/20090909-4-jp-2010.00011

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…Shim, E.S. et al presented a stable vision system for mobile robots using encoder data [2]. It detects a target within a few meters and maintains it fixed in the center of the image frame during locomotion.…”

Section: Introductionmentioning

confidence: 99%

Sensor Data Fusion Using Unscented Kalman Filter for VOR-Based Vision Tracking System for Mobile Robots

Anjum

Ahmad

Bona

et al. 2014

Towards Autonomous Robotic Systems

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This paper presents sensor data fusion using Unscented Kalman Filter (UKF) to implement high performance vestibulo-ocular reflex (VOR) based vision tracking system for mobile robots. Information from various sensors is required to be integrated using an efficient sensor fusion algorithm to achieve a continuous and robust vision tracking system. We use data from low cost accelerometer, gyroscope, and encoders to calculate robot motion information. The Unscented Kalman Filter is used as an efficient sensor fusion algorithm. The UKF is an advanced filtering technique which outperforms widely used Extended Kalman Filter (EKF) in many applications. The system is able to compensate for the slip errors by switching between two different UKF models built for slip and no-slip cases. Since the accelerometer error accumulates with time because of the double integration, the system uses accelerometer data only for the slip case UKF model. Using sensor fusion by UKF, the position and orientation of the robot is estimated and is used to rotate the camera mounted on top of the robot towards a fixed target. This concept is derived from the vestibuloocular reflex (VOR) of the human eye. The experimental results show that the system is able to track the fixed target in various robot motion scenarios including the scenario when an intentional slip is generated during robot navigation.

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Section: Introductionmentioning

confidence: 99%

Sensor Data Fusion Using Unscented Kalman Filter for VOR-Based Vision Tracking System for Mobile Robots

Anjum

Ahmad

Bona

et al. 2014

Towards Autonomous Robotic Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, the main purpose of this system is involved the camera attitude estimation, motion estimation, and 3 dimensional structure reconstructions. In 2009, Cho present the VOR based vision tracking system for the wheeled robot (Shim et al, 2009;Ouh et al, 2009). However, these systems are able to be applied only to the planar robot motion, due to the absence of the multi-axis gyroscope and tilt motion actuator.…”

Section: Introductionmentioning

confidence: 99%

Pan/Tilt Camera Control for Vision Tracking System Based on the Robot Motion and Vision Information

Park

Hwang

Bahn

et al. 2011

IFAC Proceedings Volumes

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“…Vision tracking technology is used in mobile robots to enhance their visual sensing ability and enable them to track the target continuously. Vision tracking systems using robot motion information have been researched for number of years, for instance, E. S. Shim et al (2009), and A. Lenz et al (2008).…”

Section: Introductionmentioning

confidence: 99%