Jingdao Chen

Self-corrective knowledge-based hybrid tracking system using BIM and multimodal sensors

We propose a self-corrective real-time tracking system using a hybrid approach with BLE, motion sensors, and BIM.A full-site construction test was conducted to validate the developed hybrid tracking system.The proposed hybrid tracking system presented a way to compensate for the weakness of each system component.The system components show positive effects through the interaction by reducing tracking errors up to 42%. Researchers have recently devoted considerable attention to acquiring location awareness of assets. They have explored various technologies, such as video cameras, radio signal strength indicator-based sensors, and motion sensors, in the development of tracking systems. However, each system presents unique drawbacks especially when applied in complex indoor construction environments; this paper classifies them into two categories: absolute tracking and relative tracking. By understanding the nature of problems in each tracking category, this research develops a novel tracking methodology that uses knowledge of the strengths and weaknesses of various components used in the proposed tracking system. This paper presents the development of a hybrid-tracking system that integrates Bluetooth Low Energy (BLE) technology, motion sensors, and Building Information Model (BIM). The hypothesis tested through this integration was whether such knowledge-based integration could provide a method that can correct errors found in each of the used sensing technologies and thereby improve the reliability of the tracking system. Field experimental trials were conducted in a full-scale indoor construction site to assess the performance of individual components and the integrated system. The results indicated that the addition of map knowledge from a BIM model showed the capability of correcting improbable movements. Furthermore, the knowledge-based decision making process demonstrated its capability to make positive interaction by reducing the positioning errors by 42% on average. In sum, the proposed hybrid-tracking system presented a novel method to compensate for the weakness of each system component and thus achieve a more accurate and precise tracking in dynamic and complex indoor construction sites.

Robotic sensing and object recognition from thermal-mapped point clouds

AbstractMany of the civil structures are more than half way through or nearing their intended service life; frequently assessing and maintaining structural integrity is a top maintenance priority. Robotic inspection technologies using ground and aerial robots with 3D scanning and imaging capabilities have the potential to improve safety and efficiency of infrastructure management. To provide more valuable information to inspectors and agency decision makers, automatic environment sensing and semantic information extraction are fundamental issues in this field. This paper introduces an innovative method for generating thermal-mapped point clouds of a robot’s work environment and performing automatic object recognition with the aid of thermal data fused to 3D point clouds. The laser scanned point cloud and thermal data were collected using a custom-designed mobile robot. The multimodal data was combined with a data fusion process based on texture mapping. The automatic object recognition was performed by two processes: segmentation with thermal data and classification with scanned geometric features. The proposed method was validated with the scan data collected in an entire building floor. Experimental results show that the thermal integrated object recognition approach achieved better performance than a geometry only-based approach, with an average recognition accuracy of 93%, precision of 83%, and recall rate of 86% for objects in the tested environment including humans, display monitors and light fixtures.

Automated Point Cloud Registration Using Visual and Planar Features for Construction Environments

AbstractBecause of the limited view of data of each single laser scan, multiple scans are required to cover all scenes of a large construction site, and a registration process is needed to merge th...

Real-Time 3D Crane Workspace Update Using a Hybrid Visualization Approach

AbstractCrane operators often face poor visibility and collision hazards during lifting operations. Dynamic three-dimensional (3D) modeling of the crane workspace helps to alleviate this by identif...

SLAM-Driven Intelligent Autonomous Mobile Robot Navigation for Construction Applications

The demand for construction site automation with mobile robots is increasing due to its advantages in potential cost-saving, productivity, and safety. To be realistically deployed in construction sites, mobile robots must be capable of navigating in unstructured and cluttered environments. Furthermore, mobile robots should recognize both static and dynamic obstacles to determine drivable paths. However, existing robot navigation methods are not suitable for construction applications due to the challenging environmental conditions in construction sites. This study introduces an autonomous as-is 3D spatial data collection and perception method for mobile robots specifically aimed for construction job sites with many spatial uncertainties. The proposed Simultaneous Localization and Mapping (SLAM)-based navigation and object recognition methods were implemented and tested with a custom-designed mobile robot platform, Ground Robot for Mapping Infrastructure (GRoMI), which uses multiple laser scanners and a camera to sense and build a 3D environment map. Since SLAM did not detect uneven surface conditions and spatiotemporal objects on the ground, an obstacle detection algorithm was developed to recognize and avoid obstacles and the highly uneven terrain in real time. Given the 3D real-time scan map generated by 3D laser scanners, a path-finding algorithm was developed for autonomous navigation in an unknown environment with obstacles. Overall, the 3D color-mapped point clouds of construction sites generated by GRoMI were of sufficient quality to be used for many construction management applications such as construction progress monitoring, safety hazard identification, and defect detection.