In addition, a Self-Adaptive Feature Transform (SAFT) was proposed to improve such an RTLS’s robustness in challenging environments. The achieved marker position measurement accuracy ranges from 0.039m to 0.186m, proving the method’s feasibility and applicability in providing real-time localization for a wide range of applications. The system’s localization accuracy was experimentally evaluated with a set of visual landmarks. To provide a versatile Real-Time Location System (RTLS), an occupancy grid mapping enhanced visual SLAM (vSLAM) was developed to support path planning and continuous navigation that cannot be implemented directly on vSLAM’s original feature map. First, this dissertation advanced Simultaneous Localization and Mapping (SLAM) techniques for convenient and low-cost locating capabilities compared with previous work. In an effort to advance the fundamental computational building blocks for such applications, this dissertation explored three categories of scene understanding capabilities: 1) Localization and mapping for geometric scene understanding that enables a mobile agent (e.g., robot) to locate itself in an environment, map the geometry of the environment, and navigate through it 2) Object recognition for semantic scene understanding that allows for automatic asset information extraction for asset tracking and resource management 3) Distributed coupling analysis for system-level scene understanding that allows for discovery of interdependencies between different built-environment processes for system-level performance analyses and response-planning. Examples of such potential solutions include the introduction of automation in environment monitoring, infrastructure inspections, asset management, and building performance analyses.
Recent advances in robotics and enabling fields such as computer vision, deep learning, and low-latency data passing offer significant potential for developing efficient and low-cost solutions for improved construction and operation of the built environment.
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