Depth Ranging Performance Evaluation and Improvement for RGB-D Cameras on Field-Based High-Throughput Phenotyping Robots

RGB-D cameras have been successfully used for indoor High-ThroughPut Phenotyping (HTPP). However, their capability and feasibility for in-field HTPP applications still need to be evaluated. To solve the problem, we evaluate the depth-ranging performances of a consumer-level RGB-D camera (RealSense D435i) under in-field scenarios. First, we focus on determining their optimal ranging areas for different crop organs. Second, based on the evaluation results, we analyze the influences of light intensity on depth measurements and propose a brightness-and-distance based Support Vector Regression Strategy, to compensate the ranging error. Finally, we give an intuitive accuracy ranking diagram for RealSense D435i under natural lighting intensities. Experimental results show that: 1) RealSense D435i has good ranging performances on in-field HTPP. 2) Our error compensation model can effectively reduce the influences of lighting intensity and target distance.