High performance vision tracking system for mobile robot using sensor data fusion with Kalman filter

Park, Jaehong; Hwang, Wonsang; Kwon, Hyun-il; Kim, Jong-hyeon; Lee, Chang-Hun; Anjum, Muhammad Latif; Kim, Kwangsoo; Cho, Dong‐il Dan

doi:10.1109/iros.2010.5650367

Cited by 8 publications

(2 citation statements)

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“…Kalman filtering has a long history of successful applications in which unknown variables, corrupted by Gaussian noise, can be approximately determined from observations. Applications include tracking [21][22][23], and short-term predictions [24]. For wind speed prediction, the Kalman filter has been used in different applications.…”

Section: Introductionmentioning

confidence: 99%

Short-term wind speed prediction based on robust Kalman filtering: An experimental comparison

2015

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Section: Introductionmentioning

confidence: 99%

Short-term wind speed prediction based on robust Kalman filtering: An experimental comparison

2015

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“…Jaehong Park et al developed a high performance vision tracking system for mobile robots using sensor data fusion via Kalman filter [1]. The robot motion information is computed by low cost accelerometer data, gyroscope data, and encoder data.…”

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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A novel line of sight control system for a robot vision tracking system, using vision feedback and motion-disturbance feedforward compensation

et al. 2012

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SUMMARYThis paper presents a novel line of sight control system for a robot vision tracking system, which uses a position feedforward controller to preposition a camera, and a vision feedback controller to compensate for the positioning error. Continuous target tracking is an important function for service robots, surveillance robots, and cooperating robot systems. However, it is difficult to track a specific target using only vision information, while a robot is in motion. This is especially true when a robot is moving fast or rotating fast. The proposed system controls the camera line of sight, using a feedforward controller based on estimated robot position and motion information. Specifically, the camera is rotated in the direction opposite to the motion of the robot. To implement the system, a disturbance compensator is developed to determine the current position of the robot, even when the robot wheels slip. The disturbance compensator is comprised of two extended Kalman filters (EKFs) and a slip detector. The inputs of the disturbance compensator are data from an accelerometer, a gyroscope, and two wheel-encoders. The vision feedback information, which is the targeting error, is used as the measurement update for the two EKFs. Using output of the disturbance compensator, an actuation module pans the camera to locate a target at the center of an image plane. This line of sight control methodology improves the recognition performance of the vision tracking system, by keeping a target image at the center of an image frame. The proposed system is implemented on a two-wheeled robot. Experiments are performed for various robot motion scenarios in dynamic situations to evaluate the tracking and recognition performance. Experimental results showed the proposed system achieves high tracking and recognition performances with a small targeting error.

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High performance vision tracking system for mobile robot using sensor data fusion with Kalman filter

Cited by 8 publications

References 12 publications

Short-term wind speed prediction based on robust Kalman filtering: An experimental comparison

Short-term wind speed prediction based on robust Kalman filtering: An experimental comparison

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

A novel line of sight control system for a robot vision tracking system, using vision feedback and motion-disturbance feedforward compensation

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