Tang, J; Wang, M; Luo, H; Wong, PKY; Zhang, X; Chen, W; Cheng, JCP; (2023) Full-body pose estimation for excavators based on data fusion of multiple onboard sensors. Automation in Construction , 147 , Article 104694. 10.1016/j.autcon.2022.104694.

Preview

Text full-body pose estimation for excavators based on data fusion of multiple onboard sensors-submission.pdf - Accepted Version Download (2MB) | Preview

Abstract

To reduce machine-related accidents on sites, automatically monitoring the full-body poses of operating heavy machines is crucial. Conventional pose estimation systems relying on homogeneous sensors are vulnerable to negative environmental impacts, leading to inaccurate and unstable estimation of machine states. Hence, a fullbody pose estimation framework is proposed for excavators, with a data fusion strategy to utilize different types of onboard sensors for enhanced accuracy and robustness. Specifically, a non-invasive onboard visual-inertial sensor system is designed for data fusion. Then, through competitive and complementary data fusion, the keypoints describing the full-body poses of the excavator are tracked in 3D space. Especially, an EKF-based localization algorithm is developed for optimized multi-keypoint tracking, which is verified to improve the accuracy and robustness of pose estimation by a real-world excavator case study. The proposed sensor-fusion method can effectively improve operational safety, by accurately monitoring the motion of heavy machines operating on construction sites.

Download activity - last month

Export as CSVXMLJSON

Download activity - last 12 months

Export as JSONXMLCSV

Downloads by country - last 12 months

Export as JSONXMLCSV

Archive Staff Only

View Item