LiDAR-based benchmark approach development and validation for unloading-on-the-go systems incorporating stereo camera-based perception

This paper describes the design, algorithm development, and experimental validation of a novel LiDAR-based benchmark system to evaluate the in-situ performance of combine harvester grain unloading on-the-go automation systems that incorporate stereo camerabased perception. To find the appropriate sensor for the benchmark system, different LiDAR sensors were compared based on their field-of-view and performance in the presence of dust. A data processing framework was developed for the benchmark perception system to extract the reference grain fill map from the raw LiDAR point clouds via data preprocessing, cart boundary detection, cart tarp removal, and fill map generation. The LiDARbased benchmark system provides an accurate reference for the in-cart grain profile estimation with a height measurement error of less than 0.03 m. During unloading-on-the-go, this benchmark system runs simultaneously with the stereo-camera based system to provide the benchmark data via post-processing from in-field testing. The comparison provides quantitative analysis of the stereo-camera based system including the spatial distribution and the temporal progression of the measurement error in different unloading scenarios. Experimental results demonstrated that the proposed benchmark system provides consistent benchmark data with over 80% perception coverage of the grain cart in both clear and dusty environments. & COPY; 2023 IAgrE. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper describes a novel LiDAR-based benchmark system for evaluating the in-situ performance of combine harvester grain unloading on-the-go automation systems. The appropriate sensor for the benchmark system was chosen by comparing different LiDAR sensors based on their field-of-view and performance in dusty environments. The benchmark system provides accurate reference grain fill maps and runs simultaneously with the stereo-camera based system during unloading-on-the-go, providing benchmark data for quantitative analysis.