Development of a real-time 3D camera based on micro-electromechanical systems mirrors

Developments in light detection and ranging (LiDAR) technology have brought innovations in three-dimensional (3D) measurement. After mechanical laser scanners were introduced in the 1990s, the speed and point density of LiDAR measurements have increased considerably with the developments in photon imagers. On the other hand, lightweight and small-size LiDAR sensors and their integrated use with other related sensors have made the use of LiDAR widespread for mapping and navigation purposes on mobile platforms. Matrix imaging LiDAR cameras and solid-state laser scanners have no or fewer moving parts for measurement, and are unaffected by vibrations. They are usually used in mobile mapping, driverless vehicle navigation, and mobile robot navigation. Pulse or phase-shift methods are used to measure the distance from the LiDAR instrument to the scan point. The measured scan point direction is determined by the orientation angles of the beam in scanners, focal length, and pixel positions in matrix viewers, and instrument-centered 3D coordinates are calculated. LiDAR tools have their own specific capabilities and limitations. Therefore, the selection of the appropriate LiDAR for any application is very important. In this study, after LiDAR principles are introduced, scanning LiDAR and pixel-based matrix imager LiDAR methods used to measure 3D point clouds are technically examined and analyzed.

Aerosol plays an important role in regularization of the earth’s energy balance. Compared with light detection and ranging (LIDAR) pointing vertically, scanning LIDAR is a useful tool for detection of the spatial and temporal distribution of aerosols. A scanning LIDAR is constructed in which a compact 532-nm laser is bound to the telescope. Under the command from the serial port, the LIDAR can observe in different scanning modes. We introduce the structure and key parameters of the scanning LIDAR, and then verify its measurement ability by comparison with the Rayleigh–Raman–Mie LIDAR. Observation of a plume emission from a chemical factory is conducted in the northern suburbs of Nanjing, China. In order to obtain the distribution of the plume emission, the slope method and Fernald method are combined to invert the extinction coefficient of the plume. Analysis of the data shows that the scanning LIDAR can be used to monitor the relative emission concentrations of pollutants and depict the process of the pollutants’ diffusion. The scanning LIDAR can also be used to measure the three-dimensional variation of the extinction coefficient by automatic volume scanning.

The measurement of discontinuity (joint) orientations is critical in assessing the stability of discontinuous rock slopes. The discontinuity orientations are used as input to all discontinuous modeling programs and methods, including kinematic screening methods and limited equilibrium sliding methods. ### Manual Discontinuity Measurements Traditionally, discontinuity orientations are measured manually using a compass (Figure 1). The drawbacks of this method include the fact that these measurements are slow, are tedious, often subject the user to physical danger (from rock falls and/or the need to scale slopes for measurements at heights), and may be inaccurate due to sampling biases when measurements are restricted to accessible areas only. Figure 1. Manual measurements of joints are slow, tedious, expose the user to danger, and are often limited to locations that are easily accessible. ### LiDAR Measurements Terrestrial LiDAR (light detection and ranging) scanners (Figure 2) can return detailed three-dimensional (3-D) maps or point clouds of rock slopes. These include highly accurate maps of planar features such as joints and other discontinuities. Joint orientations can be measured on these point clouds in various ways. LiDAR scanners are increasingly being used for the purpose of measuring discontinuity orientations (Bulut and Tudes, 1996; Feng, 2001; Post, 2001; Donovan et al., 2005; Strouth and Eberhard, 2006; Haneberg, 2008; Olariu et al., 2008; Lato et al., 2009; Sturzeinegger and Stead, 2009; Gigli and Casagli, 2011; and Otoo et al., 2011). Figure 2. Leica ScanStation II in Saudi Arabia. #### Systematic LiDAR Orientation Measurements One way to automatically generate orientations from LiDAR scans is to use advanced algorithms to create solid models (polygonal models) where each polygon represents a planar face. The typical LiDAR measurements work well in general when using scans from vegetation-free continuous rock cuts or slopes where the exposure is entirely composed of discontinuity surfaces. Where vegetation is present, or blast- or …

This paper presents an offline ground truth generation method using LIDAR(Light Detection and Ranging) scans and odometry. The generated ground truth allows quantitative evaluation of the performance of self-localization methods in urban areas where GNSS(Global Navigation Satellite System) cannot be trusted. The proposed method determines the vehicle pose (position and orientation) by aligning the LIDAR input with previously collected point cloud data. However, as alignment convergences are affected by the environment around the vehicle during each LIDAR scan, it can be erroneous. Incorrect estimates are removed and poses are interpolated by relying on odometry; which is locally accurate. A step by step optimization approach is adopted to yield the most accurate result. Experiments performed in a typical urban environment, with many buildings and surrounding obstacles, demonstrated the effectiveness of the proposed method.

A wind measurement campaign using a single scanning light detection and ranging (LiDAR) device was conducted at the Hazaki Oceanographical Research Station (HORS) on the Hazaki coast of Japan to evaluate the performance of the device for coastal wind measurements. The scanning LiDAR was deployed on the landward end of the HORS pier. We compared the wind speed and direction data recorded by the scanning LiDAR to the observations obtained from a vertical profiling LiDAR installed at the opposite end of the pier, 400 m from the scanning LiDAR. The best practice for offshore wind measurements using a single scanning LiDAR was evaluated by comparing results from a total of nine experiments using several different scanning settings. A two-parameter velocity volume processing (VVP) method was employed to retrieve the horizontal wind speed and direction from the radial wind speed. Our experiment showed that, at the current offshore site with a negligibly small vertical wind speed component, the accuracy of the scanning LiDAR wind speeds and directions was sensitive to the azimuth angle setting, but not to the elevation angle setting. In addition to the validations for the 10-minute mean wind speeds and directions, the application of LiDARs for the measurement of the turbulence intensity (TI) was also discussed by comparing the results with observations obtained from a sonic anemometer, mounted at the seaward end of the HORS pier, 400 m from the scanning LiDAR. The standard deviation obtained from the scanning LiDAR measurement showed a greater fluctuation than that obtained from the sonic anemometer measurement. However, the difference between the scanning LiDAR and sonic measurements appeared to be within an acceptable range for the wind turbine design. We discuss the variations in data availability and accuracy based on an analysis of the carrier-to-noise ratio (CNR) distribution and the goodness of fit for curve fitting via the VVP method.

Simulation of multi-platform LiDAR for assessing total leaf area in tree crowns

Three-dimensional (3D) urban reconstruction becomes increasingly crucial in many application areas, such as entertainment, urban planning, digital mapping. To achieve photorealistic 3D urban reconstruction, the detailed reconstruction of building facades is the key. Light Detection and Ranging (LiDAR) point clouds and images are the two most important data types for 3D urban reconstruction, which are complementary regarding data characteristic. LiDAR scans are sparse and noisy but contain the precise depth data, whereas images can offer the color and high-resolution data but no depth information. In recent years, an increasing number of studies show that the fusion of LiDAR point clouds and images can attain better 3D reconstruction results than a single data type. In this paper, we aim to provide a systematic review of the research in the area of the 3D facade reconstruction based on the fusion of LiDAR and images. The reviewed studies are classified by the different usage of images in the reconstruction process. We hope that this research could help future researchers have a more clear understanding of how existing studies leverage the data in LiDAR scans and images and promote more innovations in this area.

3D LIDARs (Light Detection and Ranging) with 1.5μm nanosecond pulse lasers have been increasingly used in different applications. The main reason for their popularity is that these LIDARs have high performance while at the same time can be made eye-safe. Because the laser hazard effect on eyes or skin at this wavelength region (<1.4μm) is mainly from the thermal effect accumulated from many individual pulses over a period of seconds, scanning can effectively reduce the laser beam hazard effect from the LIDARs. Neptec LIDARs have been used in docking to the International Space Station, military helicopter landing and industrial mining applications. We have incorporated the laser safety requirements in the LIDAR design and conducted laser safety analysis for different operational scenarios. While 1.5μm is normally said to be the eye-safe wavelength, in reality a high performance 3D LIDAR needs high pulse energy, small beam size and high pulse repetition frequency (PRF) to achieve long range, high resolution and high density images. The resulting radiant exposure of its stationary beam could be many times higher than the limit for a Class 1 laser device. Without carefully choosing laser and scanning parameters, including field-of-view, scan speed and pattern, a scanning LIDAR can’t be eye- or skin-safe based only on its wavelength. This paper discusses the laser safety considerations in the design of eye-safe scanning LIDARs, including laser pulse energy, PRF, beam size and scanning parameters in two basic designs of scanning mechanisms, i.e. galvanometer based scanner and Risley prism based scanner. The laser safety is discussed in terms of device classification, nominal ocular hazard distance (NOHD) and safety glasses optical density (OD).

Investigating the three-dimensional structure of mangrove forests is critical for their conservation and restoration. However, mangrove forests are difficult to survey in the field, and their 3D structure is poorly understood. Light detection and ranging (LiDAR) is considered an accurate and dependable method of measuring the 3D structure of mangrove forests. This study aimed to find a suitable LiDAR platform for obtaining attributes such as breast height diameter and canopy area, as well as for measuring a digital terrain model (DTM), the base data for hydrological analysis. A mangrove forest near the mouth of the Oura River in Aza-Oura, Nago City, Okinawa Prefecture, Japan, was studied. We used data from terrestrial LiDAR scanning “TLS” and unmanned aerial vehicle (UAV) LiDAR scanning “ULS” as well as data merged from TLS and ULS “Merge”. By interpolating point clouds of the ground surface, DTMs of 5 cm × 5 cm were created. DTMs obtained from ULS could not reproduce the heaps of Thalassina anomala or forest floor microtopography compared with those obtained from TLS. Considering that ULS had a few point clouds in the forest, automatic trunk identification could not be used to segment trees. TLS could segment trees by automatically identifying trunks, but the number of trees identified roughly doubled that of the visual identification results. The number of tree crowns identified using TLS and ULS was approximately one quarter of those identified visually, and many of them were larger in area than the visually traced crowns. The accuracy of tree segmentation using the canopy height model (CHM) was low. The number of canopy trees identified using Merge produced the best results, accounting for 61% of the visual identification results. Results of tree segmentation by CHM suggest that combining TLS and ULS measurements may improve tree canopy identification. Although ULS is a promising new technology, its applications are clearly limited, at least in mangrove forests such as the Oura River, where Bruguiera gymnorhiza is dominant. Depending on the application, using different LiDAR platforms, such as airborne LiDAR scanning, UAV LiDAR scanning, and TLS, is important. Merging 3D point clouds acquired by different platforms, as proposed in this study, is an important option in this case.

Light Detection and Ranging (LiDAR) refers to a range imaging method for distance objects based on the principle of laser ranging. LiDAR environmental mapping technology is often highly praised for its precise mapping information with intricate features for various detection or tracking based applications. The research proposes a novel method for independently identifying and filtering noise clusters in 2-Dimensional (2D) LiDAR scans based on 2 distinct clustering algorithms of K-Means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Results show DBSCAN to be the better choice as it is more robust and resistance to noise and outliers in the dataset and is capable of identifying clusters of any shape making it more versatile. Furthermore, to address the issue of dead zones present in LiDAR scanning, an innovative solution based on interpolating the discontinuous spatial results of the LiDAR scanning result to further reconstruct a 3-Dimensional (3D) viewing model by stacking multiple copies of 2D LiDAR scanning results with varying elevation is demonstrated by the results of the study to be a viable economical alternative for 3D LiDAR mapping.

This paper introduces an autonomous integrated indoor navigation system for ground vehicles that fuses inertial sensors, light detection and ranging (LiDAR) sensors, received signal strength (RSS) observations in wireless local area networks (WLANs), odometry, and predefined occupancy floor maps. This paper proposes a solution for the problem of automatic self-alignment and position initialization indoors under the absence of an absolute navigation system such as Global Navigation Satellite Systems (GNSS). The initial tilt angles (roll and pitch) are estimated by an extended Kalman filter (EKF) that uses two horizontal accelerometers as measurements. The initial position and heading estimation is performed using a subimage matching algorithm based on normalized cross-correlation between projected 2-D LiDAR scans and an occupancy floor map of the environment. The ambiguities in position/heading initialization are resolved using RSS. The proposed position/heading estimation module is also utilized in navigation mode as a source of absolute position/heading updates to EKF for enhanced observability. The state predictor is an enhanced 3-D inertial navigation system that utilizes low-cost microelectromechanical system (MEMS)-based reduced inertial sensor set aided by vehicle odometry. In navigation mode, LiDAR scans are used to estimate the vehicle's relative motions using an inertial-aided iterative closest point algorithm. To fuse all available measurements, a multirate multimode EKF design is proposed to correct navigation states and estimate sensor biases. The developed system was tested under a real indoor office environment covered by an IEEE 802.11 WLAN on a mobile robot platform equipped with MEMS inertial sensors, a WLAN interface, a 2-D LiDAR scanner, and a quadrature encoder. Results demonstrated the capabilities of the self-alignment and initialization module and showed average submeter-level positioning accuracy.

The study explores the potential of LiDAR (Light Detection and Ranging) technology as a non-invasive alternative for measuring spinal alignment, particularly focusing on the correlation between LiDAR generated data and traditional X-ray measurements. The research involved 275 patients who underwent both full spine X-rays and LiDAR scans. The study compared measurements of Cobb angle, lumbar lordosis, and thoracic kyphosis derived from X-rays and the Spine3D LiDAR system by Sensor Medica. The results demonstrated a strong positive correlation between LiDAR and X-ray measurements across all conditions. The findings suggest that while LiDAR cannot replace X-rays for initial diagnostic purposes, it offers a promising tool for ongoing monitoring of spinal deformities, potentially reducing the frequency of exposure to ionising radiation, and improving patient compliance through engagement. This research highlights the potential for integrating LiDAR technology into clinical practice

Although the use of both drones and LiDAR (light detection and ranging) has become common in archaeology in recent years, LiDAR scanning from drones is still in its infancy. The technological development related to drones as well as laser scanner instruments has gradually reached the point where these can be integrated. In this paper we present the results from a test where the applicability of LiDAR used from a drone was studied. The study had two objectives – both based on comparative studies: (i) whether LiDAR from drones represents an improvement in terms of detection success; and (ii) whether LiDAR from drones can increase the quality of the documentation of archaeological features and their physical properties based on remote sensing. A modest improvement of detection success was found, but was not as convincing as one would perhaps expect given the relatively large increase in terms of ground points. This has led us to the conclusion that very dense vegetation obstructs laser beams from reaching all the way to the bare earth. As regards accuracy in documenting archaeological features, the study showed more significant improvements. The last part of the paper is dedicated to a discussion of the pros and cons of using LiDAR from drones compared to conventional airborne laser scanning from aeroplanes or helicopters. The main advantages concern flexibility, low flight altitude and small laser footprint as well as the advantages of a far‐reaching field of view. The disadvantages are related to price, battery capacity, size of area and especially the requirement of line of sight between the drone operator and the drone, a fact that restricts the efficiency in terms of mapping large areas. Nevertheless, the final conclusion is that LiDAR from drones has the potential to make a substantial improvement to archaeological remote sensing.

Routine bridge inspections require labor intensive and highly subjective visual interpretation to determine bridge deck surface condition. Light Detection and Ranging (LiDAR) a relatively new class of survey instrument has become a popular and increasingly used technology for providing as-built and inventory data in civil applications. While an increasing number of private and governmental agencies possess terrestrial and mobile LiDAR systems, an understanding of the technology’s capabilities and potential applications continues to evolve. LiDAR is a line-of-sight instrument and as such, care must be taken when establishing scan locations and resolution to allow the capture of data at an adequate resolution for defining features that contribute to the analysis of bridge deck surface condition. Information such as the location, area, and volume of spalling on deck surfaces, undersides, and support columns can be derived from properly collected LiDAR point clouds. The LiDAR point clouds contain information that can provide quantitative surface condition information, resulting in more accurate structural health monitoring. LiDAR scans were collected at three study bridges, each of which displayed a varying degree of degradation. A variety of commercially available analysis tools and an independently developed algorithm written in ArcGIS Python (ArcPy) were used to locate and quantify surface defects such as location, volume, and area of spalls. The results were visual and numerically displayed in a user-friendly web-based decision support tool integrating prior bridge condition metrics for comparison. LiDAR data processing procedures along with strengths and limitations of point clouds for defining features useful for assessing bridge deck condition are discussed. Point cloud density and incidence angle are two attributes that must be managed carefully to ensure data collected are of high quality and useful for bridge condition evaluation. When collected properly to ensure effective evaluation of bridge surface condition, LiDAR data can be analyzed to provide a useful data set from which to derive bridge deck condition information.

A typical LIDAR (Light Detection and Ranging) scan contains hundreds of millions of points. As such, the visualization of LIDAR point clouds poses a significant challenge in data analysis. One solution is to display LIDAR point clouds on a large display wall with an array of LCD monitors. This provides researchers with a high-resolution display environment for looking at and studying large datasets. In this paper, we present a case study that visualizes LIDAR point clouds on a tiled display wall termed HIPerDisplay (Highly Interactive Parallelized Display). It has twenty 24-inch LCDs with a total resolution of 46 megapixels. Interaction between the user and the display wall is achieved by using a video camera system that is able to track the position of a hand-held light ball device. A user holds it to manipulate point clouds on HIPerDisplay. Case studies are conducted to study the LIDAR scans of slopes in the Houshanyue mountain areas in Taiwan. Experiments were conducted to examine the advantages of using the HIPerDisplay for point clouds in data post-processing. The experiments assess two tasks for manipulating point cloud data designed to evaluate the efficiency of the interactive devices. To evaluate the efficiency of the system, a group of thirty graduate students participated in the experiment. User surveys were performed to evaluate the efficiency of the system and to discover the users' opinions about using the interactive device in a large display environment. The results showed that the participants preferred to perform LIDAR data operation tasks on a high-resolution large display environment rather than on a single monitor. The results also showed that HIPerDisplay offered superior performance for the processing of large LIDAR datasets.