Hawk‐eye‐inspired perception algorithm of stereo vision for obtaining orchard 3D point cloud navigation map

Abstract

Abstract The binocular stereo vision is the lowest cost sensor for obtaining 3D information. Considering the weakness of long‐distance measurement and stability, the improvement of accuracy and stability of stereo vision is urgently required for application of precision agriculture. To address the challenges of stereo vision long‐distance measurement and stable perception without hardware upgrade, inspired by hawk eyes, higher resolution perception and the adaptive HDR (High Dynamic Range) were introduced in this paper. Simulating the function from physiological structure of ‘deep fovea’ and ‘shallow fovea’ of hawk eye, the higher resolution reconstruction method in this paper was aimed at accuracy improving. Inspired by adjustment of pupils, the adaptive HDR method was proposed for high dynamic range optimisation and stable perception. In various light conditions, compared with default stereo vision, the accuracy of proposed algorithm was improved by 28.0% evaluated by error ratio, and the stability was improved by 26.56% by disparity accuracy. For fixed distance measurement, the maximum improvement was 78.6% by standard deviation. Based on the hawk‐eye‐inspired perception algorithm, the point cloud of orchard was improved both in quality and quantity. The hawk‐eye‐inspired perception algorithm contributed great advance in binocular 3D point cloud reconstruction in orchard navigation map.

Keywords

• adaptive high dynamic range
• binocular stereo vision
• hawk‐eye‐inspired perception
• point cloud of orchard
• super‐resolution generative adversarial network