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The variation of construction machine poses is one of the main causes for interactive on-site safety issues such as struck-by hazards. With the aim to reduce such hazards, we propose a framework for predicting construction machine poses based on historical motion data and activity attributes. After building a machine motion dataset, we develop a keypoint-based method for recognizing machine activities considering working patterns and interaction characteristics. The recognized activity information is then incorporated with historical pose data to predict future machine poses through a type of recurrent neural network (RNN), named Gated Recurrent Unit (GRU). In experiments of using excavators as the objects, our framework achieves decent performance for machine pose prediction, which is further improved by incorporating activity information, reaching an average percentage of correct keypoints (PCK) of 90.22%. The results indicate the high potential of our framework in predicting construction machine poses and improving on-site safety. • Proposed a GRU-based method for machine pose prediction using motion capture data • Improved machine pose prediction results by keypoint-based activity recognition • Considered non-geometric data like machine interaction and activity type • A rollback method greatly reduced the influence of uncertainty in activity recognition • Achieved an average accuracy over 90% for machine pose prediction [ABSTRACT FROM AUTHOR]

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 full-body 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. • A pose estimation framework for excavators proposed using visual-inertial sensor fusion method. • A non-invasive onboard multi-sensor system designed for data fusion to track motions. • An EKF-based algorithm developed to track multi-keypoint of excavators competitively. • Improved accuracy and robustness in keypoint tracking, confirmed by real-life excavator case study. [ABSTRACT FROM AUTHOR]

Monitoring unsafe operations of construction machines is important for reducing accidents on sites. To automate safety monitoring, this paper studies pose estimation of construction machines, which describes their motions in 3D space. Inertial Measurement Unit (IMU) is considered accurate, cost-saving and user-friendly for estimating the pose of machines. Nevertheless, the complexity and instability of construction sites hinder the application of IMU to construction machines. Hence, this study first defines the information requirements of IMU-based full-body pose estimation for construction machines. Then, an efficient method is developed to generate the trajectories of a machine based on kinematics. Moreover, an optimal installation scheme for IMU sensors is investigated systematically to optimize the location and number of IMUs to install. Overall, this study contributes an IMU-based method to provide full-body poses of machines without environmental constraints on real construction sites, and a theoretical basis of optimal IMU installation strategy for reliable motion-related analyses. • The information requirements are defined on IMU-based analysis of construction machine poses. • A trajectory generation method is proposed to describe the change of joint angles. • The practicability and performance of the proposed approach are fully verified in real scenarios. • The average RMSE between the estimated pose and ground truth is 1.71 degrees. • An optimal IMU installation strategy is proposed including location and number. [ABSTRACT FROM AUTHOR]