Positioning and navigation of mobile robot with asynchronous fusion of binocular vision system and inertial navigation system

Abstract

Binocular stereovision-based positioning and inertial measurement-based positioning have their respective limitations. Asynchronous fusion of a binocular vision system and an inertial navigation system (INS) is therefore introduced to global positioning system-denied environments with a fuzzy map. It aims to provide a sequential and progressive update with regard to mobile robot positioning and navigation. The system consists of two off-the-shelf cameras and a low-cost inertial measurement unit (IMU). The localization procedure fuses the inertial data from the IMU and the absolute position data from the binocular vision system based on corners. The main contribution of this article is a novel fusion method adaptive to different data rates at which the two modules operate. Utilization of an asynchronous Kalman filter is proposed to fuse the results from the two modules, which can achieve intermittent correction for INS localization. Experiments were carried out in an indoor laboratory environment where dynamic tests validated the reliability and effectiveness of the proposed asynchronous fusion algorithm.

A Review of Mobile Robot Navigation System for Volcano Monitoring Application

“…𝑣 𝑙 = 𝑣 + 𝑐𝜑 (6) 𝑣 𝑟 = 𝑣 − 𝑐𝜑 (7) where v is velocity, c is coefficient vaue and φ is yaw angle obtained by the IMU [16], [70], [90], [91].…”

“…The robot parameter for odometry [70], [71], [72], [73], [74] of Robovolc was as follow: wheels radius: R1 = 0.21m, R2 = 0.21m, wheelbase: L = 0.82m, while for EKF algorithm [75], [76], [77], [78], [79] was used DGPS [14], [80]. Meanwhile, ARTEMIS 0.89 x 0.61 x 0.38 m of dimension, which has 700 MHz Pentium III PC -104 onboard computer, Crossbow AHRS-400 INS, a tachometer to measure wheel angular velocity, 20 cm resolution DGPS, and Futaba steering and throttle control servos, was tested on a flat, bumpy terrain covered with grass [46].…”

A Low-Cost and Efficient Indoor Fusion Localization Method

“…It can be located in all weather and terrain. Lei Cheng [ 35 ] et al proposed a fusion method that adopted the Kalman filter to fuse binocular vision and an inertial navigation system (INS). Jishi Cui [ 36 ] et al proposed an indoor localization system in which an improved Zee method and regularized particle filters were used to improve the cumulative error of the PDR algorithm.…”

Positioning, Navigation, and Book Accessing/Returning in an Autonomous Library Robot using Integrated Binocular Vision and QR Code Identification Systems

“…Vision navigation, including monocular vision, binocular vision, and multi-vision, is based on camera systems to collect surrounding information to establish environment maps for P&Ns [ 12 ]. Due to the limited field of view (FOV) of the cameras, however, vision navigation suffers from a lack of accuracy in large distance positioning, and it is typically utilized for indoor navigation [ 13 , 14 ].…”