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.

In the last two decades, airborne LiDAR, as an active remote sensing technique, has emerged as one of the most effective and reliable means of 3D point clouds collection. This paper presents a survey of the literature related to these techniques, emphasizing state-of-art trends in system design as well as data processing techniques and their impact on various applications. With the challenging requirements of airborne surveying and mapping, the novel LiDAR system design appears to improve data collection ability and derived product resolution/accuracy. Based on a full range of spectral, spatial and temporal properties of the data obtained, recently proposed methods and techniques are presented here to instigate further possibilities for land and urban topography, forest and bathymetric surveying. The conclusion discusses possible developments for more advanced high performance airborne LiDAR systems, allowing spectral detection and geometrical extraction for improved understanding of urban infrastructure planning and natural resource management.

No abstract

No abstract

Systematic errors generated from internal misalignments of a lab-built terrestrial laser scanner (TLS) need to be calibrated to improve the positional accuracy of point-cloud. Hence, an angle measurement error model was established by the ray-tracing method involving five types of mounting angle errors, which were estimated by two-face method and network method. Experimental results show that the two errors including mirror tilt error and vertical index offset error are regarded as main systematic errors for lab-built TLS, and consequently the positional accuracy of the point-cloud can be improved in horizontal and vertical directions. The proposed self-calibration method can be used for other TLS by adjusting the angle measurement error model.