Application of Binocular Vision and Diamond Search Technology in Computer Vision Navigation

Wu, Jie; Abdulla, Hussam M. Dahwa; Snáel, Václav

doi:10.1109/incos.2009.41

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…In recent years, machine vision has been paid wide attention and papers of related literature emerge in endlessly; however the results of localisation by binocular SV are relatively little. Wu et al [5] realised the recognition of target tasks, distance estimation and location by computer vision navigation for mobile robot tracking method. The paper [6, 7] proposed a method of depth detection based on the binocular significant area, using the left and right image map of deviation calculation error, hue, saturation, intensity of colour space and the mean shift algorithm for image decomposition.…”

Section: Introductionmentioning

confidence: 99%

Mobile robot indoor dual Kalman filter localisation based on inertial measurement and stereo vision

Cheng

Song

Yating

et al. 2017

CAAI Transactions on Intelligence Technology

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This study presents a novel navigation method designed to support a real-time, efficient, accurate indoor localisation for mobile robot system. It is applicable for inertial measurement units (IMU) consisting of gyroscopes, accelerometers, and magnetic besides stereo vision (SV). The current indoor mobile robot localisation technology adopts traditional active sensing devices such as laser, and ultrasonic method which belongs to the signal of localisation and navigation method which has low efficiency complex structure, and poor anti-interference ability. Through dual Kalman filter (DKF) algorithm, the accumulated error of gyroscope can be reduced, while combining with SV, mobile robot binocular SV orientation of inertial location can be realised under the DKF mechanism, which is introduced. First, high precision posture information of mobile robot can be obtained using fusing Kalman filter algorithm of accelerometer, gyroscope and magnetometer data. Second, inertial measurement precision can be optimised using Kalman filtering algorithm combined with machine vision localisation algorithm. The results indicate that the method achieves the levels of accuracy location comparable with that of the IMU/SV fusion algorithm; <0.0066 static RMS error, <0.0056 dynamic RMS error. The mobile robot using DKF algorithm of inertial navigation and SV indoor localisation is feasible.

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Section: Introductionmentioning

confidence: 99%