Coherence analysis for movement disorder motion captured by six degree-of-freedom inertial sensing

Teskey, Wesley J. E.; Elhabiby, Mohamed; El‐Sheimy, Naser; MacIntosh, Brian R.

doi:10.1515/jag-2012-0001

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Extended Kalman Filter1

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2016

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“…The EKF algorithm enables complementary compensation for each sensor's limitations, and the resulting performance of the sensor system is better than individual sensors. The motion model and the observation model in EKF are established using kinematics [24]. As EKF uses discrete models, the first and second lines represent motion model and observation model, respectively.…”

Section: Extended Kalman Filtermentioning

confidence: 99%

Extended Kalman Filter Based Mobile Robot Localization in Indoor Fire Environments

Kim¹,

Kim²

2016

IJMERR

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This paper presents localization of a mobile firefighting robot. Sensors that have been widely used for the localization in the past have shown limitations under fire environments due to low visibility and high temperatures. The extended Kalman filter was designed to accurately estimate position and orientation of the robot using relative distances to walls or objects surroundings. In addition, data from a Frequency-Modulated Continuous-Wave (FMCW) Radar, Inertial Measurement Unit (IMU) and encoders that are capable of withstanding fire environments were fused to localize the robot in indoor fire environments. For its validation, an experiment was conducted in a 2 m × 4 m area. The experimental results showed that the proposed localization method was reliable. 

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Section: Extended Kalman Filtermentioning

confidence: 99%