Reliability Analysis of Object Position Measured by Motion Stereo Considering Camera Vibration

Okazaki, Shin'ya; Tanaka, Takayuki; Kaneko, Shun’ichi; Takauji, Hidenori; Kochi, Nobuo; Yamada, Mitsuharu

doi:10.1541/ieejias.132.942

Cited by 2 publications

(1 citation statement)

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“…The disparity error distribution is of the triangular type. 40 expands the analysis for the horizontal and depth axis. 41 represent an error as a function of 3D point motion parameters, such as amplitude, frequency, and phase.…”

Section: Dynamic Cameramentioning

confidence: 99%

On Learning-Based Accuracy Estimation of 3D Reconstruction and 3D Camera Pose from RGB-D Data

Ortiz-Fernandez,

Silva,

Gonçalves

2024

Preprint

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We propose a learning-based approach to measure, model, and predict accuracy on three-dimensional reconstruction and camera pose determined from data captured by low-cost visual sensors as RGB-D cameras coupled to robotic platforms or vehicles. Basically, we start creating a ground truth of 3D points (mapping) and camera poses (localization) using an accurate set of smart markers that we specifically devised and built for this work. Using these ground truth data, a set of actual errors and accuracy are calculated during the motion of our mobile robotic platform. To the end, a modeling for the error is provided and error prediction on reconstruction and camera positioning is accurately estimated as a function of the camera’s distance, velocity, and vibration of the platform. We use a multi-layer perceptron neural network for this last step. The outputs are the root mean squared errors for the 3D reconstruction and the relative pose errors for the camera poses. Experimental results show that this approach has a prediction accuracy of ±1% for the 3D reconstruction and ±2.5% for camera poses, which shows substantially improved performance in comparison with state-of-the-art methods.

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Section: Dynamic Cameramentioning

confidence: 99%