Abstract

This paper introduces the image processing system of camera motion estimation for autonomous mobile robots. Most autonomous mobile robots need to quickly recognize the surroundings so as to move to the space. Moreover, the image processing algorithm requires low computation complexity. Accordingly, we evaluate an image processing system based on Compact and Real-time Descriptors (CARD) (1) . Proposed system has scale-invariance and rotation-invariance for local image feature. Our method uses a multi-scale image pyramid to extract coordinates for keypoint and a log-polar binning pattern for a patch around the keypoint. The results of correspondence for keypoint between two images were good enough to estimate the camera motion.

Highlights

  • Local feature descriptor is effective for visual correspondence between two images

  • This paper focuses on scale- and rotation-invariant feature descriptor by using a multi-scale image pyramid and a log-polar binning pattern, instead of a big amount of computation of gradient extremum search and a binning pattern manipulation of Scale-Invariant Feature Transform (SIFT) descriptor

  • SIFT is scale-invariant by using Difference of Gaussians (DoG) pyramid

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Summary

Introduction

Local feature descriptor is effective for visual correspondence between two images. Scale-Invariant Feature Transform (SIFT)(2) is representative method for local feature descriptors and has been verified to be useful for image recognition application. It is unsuitable to realtime processing for autonomous mobile robots. It is important for robot application to reduce computational cost for the mobile ability. This paper focuses on scale- and rotation-invariant feature descriptor by using a multi-scale image pyramid and a log-polar binning pattern, instead of a big amount of computation of gradient extremum search and a binning pattern manipulation of SIFT descriptor

Keypoint Detection
Description with Gradient Features
Rotation of a Binning Pattern
Matching by Euclidean Distance
Location Change
Experiments
Angle Change
Findings
Conclusion

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