Abstract

The emergence of Automated Guided Vehicle (AGV) has greatly increased the efficiency of the transportation industry, which put forward the urgent requirement for the accuracy and ease of use of 2D planar motion robot positioning. Multi-sensor fusion positioning has gradually become an important technical route to improve overall efficiency when dealing with AGV positioning. As a sensor directly acquiring depth, the RGB-D camera has received extensive attention in indoor positioning in recent years, while wheel odometry is the sensor that comes with most two-dimensional planar motion robots, and its parameters will not change over time. Both the RGB-D camera and the wheel odometry are commonly used sensors for indoor robot positioning, but the existing research on the fusion of RGB-D and wheel odometry is limited based on classic filtering algorithms; few fusion solutions based on optimization algorithm of them are available at present. To ensure the practicability and greatly improve the accuracy of RGB-D and odometry fusion positioning scheme, this paper proposed a tightly-coupled positioning scheme of online calibrated RGB-D camera and wheel odometry based on SE(2) plane constraints. Experiments have proved that the angle accuracy of the extrinsic parameter in the calibration part is less than 0.5 degrees, and the displacement of the extrinsic parameter reaches the millimeter level. The field-test positioning accuracy of the positioning system we proposed having reached centimeter-level on the dataset without pre-calibration, which is better than ORB-SLAM2 relying solely on RGB-D cameras. The experimental results verify the excellent performance of the frame in positioning accuracy and ease of use and prove that it can be a potential promising technical solution in the field of two-dimensional AGV positioning.

Highlights

  • With the vigorous development of industries such as autonomous driving and smart home, autonomous mobile robots have obtained increasing attention

  • For the purpose of improving the performance of fusion positioning system based on RGB-D/Odometry, and solving the practical problem of positioning in two-dimensional planar motion robot, this paper proposes a system based on extrinsic parameter calibration and tightly-coupled optimization of RGB-D camera and wheel odometry, for which extrinsic parameters are obtained according to online calibration, and positioning results based on SE(2) constraints to get better positioning performance

  • This paper proposes a tightly-coupled positioning system of online calibrated RGB-D camera and wheel odometry based on SE(2) plane constraints

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Summary

Introduction

With the vigorous development of industries such as autonomous driving and smart home, autonomous mobile robots have obtained increasing attention. For the purpose of improving the performance of fusion positioning system based on RGB-D/Odometry, and solving the practical problem of positioning in two-dimensional planar motion robot, this paper proposes a system based on extrinsic parameter calibration and tightly-coupled optimization of RGB-D camera and wheel odometry, for which extrinsic parameters are obtained according to online calibration, and positioning results based on SE(2) constraints to get better positioning performance. The fusion scheme realizes a high-accuracy, non-pre-calibration algorithm frame and solution by using the advantages of RGB-D camera/odometry and the unique characteristics of 2D planar motion robots like AGV. A complete set of RGB-D and odometry fusion positioning technology scheme based on optimization algorithm with SE(2) planar constraints is proposed, which has better accuracy performance comparing with the fusion algorithm based on filter algorithm or the classic RGB-D SLAM frame.

Preparation
Initialization Algorithm
Simulation and Real-Site Experiment Results
Simulations and Comparisons
Calibration Accuracy Test of Extrinsic Parameters
Positioning Accuracy Comparison
Comprehensive Comparison of Positioning Accuracy
Experiments
Findings
Conclusions
Full Text
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