An Entropy Analysis-Based Window Size Optimization Scheme for Merging LiDAR Data Frames

Abstract

LiDAR is a useful technology for gathering point cloud data from its environment and has been adapted to many applications. We use a cost-efficient LiDAR system attached to a moving object to estimate the location of the moving object using referenced linear structures. In the stationary state, the accuracy of extracting linear structures is low given the low-cost LiDAR. We propose a merging scheme for the LiDAR data frames to improve the accuracy by using the movement of the moving object. The proposed scheme tries to find the optimal window size by means of an entropy analysis. The optimal window size is determined by finding the minimum point between the entropy indicator of the ideal result and the entropy indicator of the actual result of each window size. The proposed indicator can describe the accuracy of the entire path of the moving object at each window size using a simple single value. The experimental results show that the proposed scheme can improve the linear structure extraction accuracy.

Detecting and Evaluating Displacement of Paving Blocks Adjacent to Deep Excavation Sites Using Terrestrial Photogrammetry

“…including the inverse analysis is also limited because it relies on those monitoring points [1,2]. Consequently, a technique for measuring the displacements induced by excavation work in the entire range or full field is crucial for ensuring construction safety [3].…”

Detecting and Evaluating Displacements of Paving Blocks Adjacent to Deep Excavation Sites Using Terrestrial Photogrammetry

“…Settlement measurements are possible with synthetic-aperture radar (SAR), but images of the area of interest may be insufficient for frequent investigation [10]. Consequently, a technique for measuring the displacements induced by excavation work in the entire range or full field is crucial for ensuring construction safety [11].…”