Time of flight (TOF) based light detection and ranging (LiDAR) is a technology for calculating distance between start/stop signals of time of flight. In lab-built LiDAR, two ranging systems for measuring flying time between start/stop signals include time-to-digital converter (TDC) that counts time between trigger signals and analog-to-digital converter (ADC) that processes the sampled start/stop pulses waveform for time estimation. We study the influence of waveform characteristics on range accuracy and precision of two kinds of ranging system. Comparing waveform based ranging (WR) with analog discrete return system based ranging (AR), a peak detection method (WR-PK) shows the best ranging performance because of less execution time, high ranging accuracy, and stable precision. Based on a novel statistic mathematical method maximal information coefficient (MIC), WR-PK precision has a high linear relationship with the received pulse width standard deviation. Thus keeping the received pulse width of measuring a constant distance as stable as possible can improve ranging precision.

Detection of water leakage is one of the most important regular tasks for underground tunnel inspection. In this paper, a new method is proposed for water leakage detection in underground tunnels by using the corrected intensity data and 3D point cloud of a terrestrial laser scanning (TLS) sensor. In the proposed method, the distance effect on TLS intensity is first corrected based on a piecewise linear interpolation by using a reference target. Then the distance corrected intensity data is used to determine the surface roughness parameter that is specially considered to correct the incident angle effect. After corrections of distance and incident angle effects, the corrected intensity data are used to detect the water leakage regions in the underground tunnel. Finally, the appendages on the tunnel wall are removed by using the 3D point cloud data to eliminate their influence on water leakage detection. To validate the feasibility of the proposed method, a case study in an underground tunnel in Beijing, China was conducted by using a TLS sensor. Experimental results have demonstrated that water leakage regions in the underground tunnel can be well extracted by using the corrected intensity data and 3D point cloud, especially when the surface roughness is considered.

Terrestrial laser scanning sensors deliver not only three-dimensional geometric information of the scanned objects but also the intensity data of returned laser pulse. Recent studies have demonstrated potential applications of intensity data from Terrestrial Laser Scanning (TLS). However, the distance and incident angle effects distort the TLS raw intensity data. To overcome the distortions, a new intensity correction method by combining the piecewise fitting and overlap-driven adjustment approaches was proposed in this study. The distance effect is eliminated by the piecewise fitting approach. The incident angle effect is eliminated by overlap-driven adjustment using the Oren–Nayar model that employs the surface roughness parameter of the scanned object. The surface roughness parameter at a certain point in an overlapped region of the multi-station scans is estimated by using the raw intensity data from two different stations at the point rather than estimated by averaging the surface roughness at other positions for each kind of object, which eliminates the estimation deviation. Experimental results obtained by using a TLS sensor (Riegl VZ-400i) demonstrate that the proposed method is valid and the deviations of the retrieved reflectance values from those measured by a spectrometer are all less than 3%.

A new classification method based on the k-plane clustering algorithm is proposed to segment the point cloud of a building roof, which is obtained from an airborne light detection and ranging (LiDAR) instrument. In the operation of laser points clustering, 3-D coordinates of laser points in the point cloud are directly used as clustering objects. Fitting planes of laser points in the clusters are generated from the obtained clustering solution, and intersecting lines of the fitting planes are calculated. Using the intersecting lines, the point cloud of the building roof is then segmented. Since calculation for the clustering objects, i.e., the normal vectors of neighboring planes of the laser points, required in the classification methods based on the fuzzy k-means clustering algorithm is avoided in the proposed method, not only is the complexity of the classification procedure reduced, but also the accuracy of classification result is improved. In addition, in the proposed method, to guarantee the effectiveness of the k-plane algorithm, the initial cluster planes are estimated from the elevation image of building roof in advance before the process of clustering operation. The proposed k-plane-based classification method is validated by using a number of real airborne LiDAR point clouds.