Object distance estimation based on frequency domain analysis using a stereo camera

Yoo, In-Sub; Seo, Seung‐Woo

doi:10.1109/icce.2015.7066437

Cited by 3 publications

(1 citation statement)

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“…In other research [6], the overlapping area is measured by two cameras when the object was located outside the optical axes to measure the object distance. In this work, they also employed the matching method to understand each image's angle and verified it in simulation, and in [7], they utilized a frequency domain from the captured image and implemented it on a stereo camera to enhance the object distance estimation. Moreover, to estimate the distance for autonomous tomato harvesting [8], they implemented the YOLO (You Only Look Once) deep learning method for detecting the object and utilized the OpenCV library Stereo SGBM algorithm to estimate the distance of each tomato.…”

Section: Introductionmentioning

confidence: 99%

Improving The Stereo Distance Measurement Accuracy on The Barelang-FC Humanoid Robot

Winarti¹,

Susanto²,

Analia³

et al. 2023

Proceedings of the 5th International Conference on Applied Engineering, ICAE 2022, 5 October 2022, Batam, Indonesia

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Distance estimation is essential in developing humanoid soccer robots. Accurate distance measurement can minimize an error while the robot is maneuvering, chasing a ball, or passing the ball to the proponent robots. Currently, stereo vision and feature matching is the conventional method to estimate the distance. Distance is estimated based on the disparity value between detected features on the stereo image. However, the matching process needs high cost computationally. Furthermore, the estimated distance based on feature matching is less accurate. Therefore, in this work, the distance estimation based on the object coordinates detected using the YOLOv3 has been proposed. Additionally, a linear regression algorithm added to improve the measurement accuracy. Several experiments have been done to verify this proposed method in realtime applications. As a result, our proposed method successfully improves the distance measurement accuracy from 86.58% to 98.01%.

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Section: Introductionmentioning

confidence: 99%