Mobile robot indoor dual Kalman filter localisation based on inertial measurement and stereo vision. Issue 4 (26th April 2019)

• Record Type: Journal Article
• Authors: Cheng, Lei; Song, Biao; Dai, Yating; Wu, Huaiyu; Chen, Yang
• Is Part Of: CAAI transactions on intelligence technology. Volume 2:Issue 4(2017)
• Page Start: 173
• Page End: 181
• Publication Date: 2019-04-26
• Subjects: sensor fusion -- navigation -- computer vision -- inertial navigation -- magnetometers -- gyroscopes -- indoor radio -- mobile robots -- robot vision -- accelerometers -- Kalman filters -- stereo image processing; mobile robot indoor dual Kalman filter localisation -- stereo vision -- novel navigation method -- efficient indoor localisation -- accurate indoor localisation -- mobile robot system -- inertial measurement units -- current indoor mobile robot localisation technology -- traditional active sensing devices -- ultrasonic method -- low efficiency complex structure -- poor anti‐interference ability -- dual Kalman filter algorithm -- mobile robot binocular SV orientation -- inertial location -- fusing Kalman filter algorithm -- magnetometer data -- inertial measurement precision -- optimised using Kalman filtering algorithm -- machine vision localisation algorithm -- IMU/SV fusion algorithm -- DKF algorithm -- inertial navigation -- SV indoor localisation; B6135 Optical, image and video signal processing -- B6140B Filtering methods in signal processing -- C3120C Spatial variables control -- C3390C Mobile robots -- C5260B Computer vision and image processing techniques; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/trit.2017.0025
• Languages: English
• ISSNs: 2468-6557

Abstract

Abstract : 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.