Depth distortion correction for consumer-grade depth cameras in crop reconstruction

Abstract

Modern consumer-grade RGB-D cameras provide intensive depth estimation and high frame rates. They have been widely used in agriculture. However, depth anamorphose occurs when using the RGB-D camera. In order to address this issue, this paper proposes a novel approach to correct the distorted depth images that fit the relationship between the true distances and the depth values from depth images. This study considers the structured light camera ASUS Xtion PRO LIVE as example to develop a system for obtaining a series of depth images at different distances from the target plane. A comparison analysis is conducted between the images before and after correction to evaluate the performance of the distortion correction. The point cloud image of corrected maize leaves is well coincident with the original plant. This approach improves the accuracy of depth measurement and optimizes the subsequent use of the depth camera in crop reconstruction and phenotyping studies.