6-DoF Object Pose Estimation Under Aerosol Conditions: Benchmark Dataset and Baseline
Heejin Yang, Seunghyeon Lee, Taejoo Kim, Yukyung Choi · Journal of Institute of Control Robotics and Systems · 2024
Research on 6-degrees of freedom (6-DoF) pose estimation has been conducted in several fields until recently. As deep learning advances, the corresponding 6-DoF pose estimations will be trained on a variety of data to make predictions after training. However, research on using datasets to perform 6-DoF pose estimation in environments such as those involving aerosols, which can be encountered in disaster areas, is presently lacking. In this paper, we propose a novel RGB-D benchmark dataset composed of paired images under both normal and aerosol conditions, also offering a baseline. We specifically address the overall pipeline, i.e., 3D model construction, sensor setup, implementation of data collection methods, the 6-DoF pose annotation process applicable to objects, and the validation of these annotations, to build the dataset proposed in this study. Using our proposed dataset, we apply previously studied 6-DoF pose estimation methodologies to benchmark and experiment on normal and aerosol situations. Our experiments reveal a substantial performance degradation in aerosol situations, and further experiments to investigate the aerosol environment reveal that performance can be improved. The aerosol conditions demonstrate the difficulty of performing 6-DoF pose estimation and the need for future research.