Pose Estimation of Automatic Battery-Replacement System Based on ORB and Improved Keypoints Matching Method

Jiabin Jiang,Pengfei Zhang,Fanyi Wang,Yongying Yang,Fan Wu

doi:10.3390/app9020237

Abstract

This paper presents an improved Oriented Feature from Accelerated Segment Test (FAST) and Rotated BRIEF (ORB) keypoints matching method for pose estimation of automatic battery-replacement systems. The key issue of the system is how to precisely estimate the pose of the camera in respect to the battery. In our system, the pose-estimation hardware module is mounted onto the robot manipulator, composed of double high brightness LED light source, one monocular camera, and two laser rangefinders. The camera is utilized to take an image of the battery, the laser rangefinders on both sides of the camera are utilized to detect the real-time distance between the battery and the pose-estimation system. The estimation result is significantly influenced by the matching result of the keypoints detected by the ORB technique. The modified matching procedure, based on spatial consistency nearest hamming distance searching method, is used to determine the correct correspondences. Meanwhile, the iterative reprojection error minimization algorithm is utilized to discard incorrect correspondences. Verified by the experiments, the results reveal that this method is highly reliable and able to achieve the required positioning accuracy. The positioning error is lower than 1 mm.

Highlights

Techniques used to estimate the camera pose, with respect to an object, are widely employed in many fields, such as industrial control, object tracking, vehicle navigation, etc
We focus on solving the problem for 3D pose estimation of textured objects with a monocular camera and laser rangefinders
Robotprovides manipulator, thedistance image of battery by theparking camera. spot, the rangefinder the real-time theaway battery is captured by suction the camera

Summary

Introduction

Techniques used to estimate the camera pose, with respect to an object, are widely employed in many fields, such as industrial control, object tracking, vehicle navigation, etc. Various optical 3D pose estimation techniques have been developed in the past few years, such as laser scanning, binocular vision, structured light, and deep learning methods. Laser scanning employs the triangulation relationship in optics [1], the conventional scanning measurement technique is not always fast enough. Binocular stereo vision technique observes one scene from different viewpoints [2] to recover the depth information of the scene. The basic principle of binocular stereo vision is similar to the mechanism of human eyes. The structured light method projects coded light onto the object [3]

Methods

Results

Conclusion