Spatio-temporal mode description in LiDAR point clouds

Analyzing objects concerning their static and dynamic change is mostly performed with IMU (Inertial Measurement Units) or GNSS (Global Navigation Satellite System) sensors fixed to a physically defined surface. We can use a total station to record additional data or support other sensors by referencing them to a homogeneous coordinate frame. LiDAR (Light Detection and Ranging) enables simultaneous, contactless, spatially connected, and time-referenced observations recording an object. All sensors share the ability to detect equivalent signal properties concerning different signal-to-noise ratios. Since object deformation is not limited to a fixed position, we must continuously model or interpret the dynamic movement within our processing to get a spatio-temporal understanding. Therefore, LiDAR offers advanced options for understanding the spatio-temporal behavior of an object with a frequency analysis executed in the or time domain. In our work, point clouds are processed in a state-of-the-art time series analysis of discretized locations in the frequency domain. Furthermore, fusing point cloud observations in a time domain approach offers a unique opportunity to analyze the spatio-temporal behavior of objects. This observation-level fusion reduces the number of required processing steps. The resulting parameter model leads to simplification of present periodic signals, yet retaining spatial and temporal consistency and streamlining subsequent interpretation     «

Analyzing objects concerning their static and dynamic change is mostly performed with IMU (Inertial Measurement Units) or GNSS (Global Navigation Satellite System) sensors fixed to a physically defined surface. We can use a total station to record additional data or support other sensors by referencing them to a homogeneous coordinate frame. LiDAR (Light Detection and Ranging) enables simultaneous, contactless, spatially connected, and time-referenced observations recording an object. All sensor...     »