Multispectral Light Detection and Ranging Technology and Applications: A Review.

Light Detection and Ranging (LiDAR) is a well-established active technology for the direct acquisition of 3D data. In recent years, the geometric information collected by LiDAR sensors has been widely combined with optical images to provide supplementary spectral information to achieve more precise results in diverse remote sensing applications. The emergence of active Multispectral LiDAR (MSL) systems, which operate on different wavelengths, has recently been revolutionizing the simultaneous acquisition of height and intensity information. So far, MSL technology has been successfully applied for fine-scale mapping in various domains. However, a comprehensive review of this modern technology is currently lacking. Hence, this study presents an exhaustive overview of the current state-of-the-art in MSL systems by reviewing the latest technologies for MSL data acquisition. Moreover, the paper reports an in-depth analysis of the diverse applications of MSL, spanning across fields of "ecology and forestry", "objects and Land Use Land Cover (LULC) classification", "change detection", "bathymetry", "topographic mapping", "archaeology and geology", and "navigation". Our systematic review uncovers the potentials, opportunities, and challenges of the recently emerged MSL systems, which integrate spatial-spectral data and unlock the capability for precise multi-dimensional (nD) mapping using only a single-data source.

Feasibility of Hyperspectral Single Photon Lidar for Robust Autonomous Vehicle Perception.

Autonomous vehicle perception systems typically rely on single-wavelength lidar sensors to obtain three-dimensional information about the road environment. In contrast to cameras, lidars are unaffected by challenging illumination conditions, such as low light during night-time and various bidirectional effects changing the return reflectance. However, as many commercial lidars operate on a monochromatic basis, the ability to distinguish objects based on material spectral properties is limited. In this work, we describe the prototype hardware for a hyperspectral single photon lidar and demonstrate the feasibility of its use in an autonomous-driving-related object classification task. We also introduce a simple statistical model for estimating the reflectance measurement accuracy of single photon sensitive lidar devices. The single photon receiver frame was used to receive 30 12.3 nm spectral channels in the spectral band 1200-1570 nm, with a maximum channel-wise intensity of 32 photons. A varying number of frames were used to accumulate the signal photon count. Multiple objects covering 10 different categories of road environment, such as car, dry asphalt, gravel road, snowy asphalt, wet asphalt, wall, granite, grass, moss, and spruce tree, were included in the experiments. We test the influence of the number of spectral channels and the number of frames on the classification accuracy with random forest classifier and find that the spectral information increases the classification accuracy in the high-photon flux regime from 50% to 94% with 2 channels and 30 channels, respectively. In the low-photon flux regime, the classification accuracy increases from 30% to 38% with 2 channels and 6 channels, respectively. Additionally, we visualize the data with the t-SNE algorithm and show that the photon shot noise in the single photon sensitive hyperspectral data contributes the most to the separability of material specific spectral signatures. The results of this study provide support for the use of hyperspectral single photon lidar data on more advanced object detection and classification methods, and motivates the development of advanced single photon sensitive hyperspectral lidar devices for use in autonomous vehicles and in robotics.

Towards 3D basic theories of plant forms.

Allometric, metabolic, and biomechanical theories are the critical foundations for scientifically deciphering plant forms. Their concrete laws, however, are found to deviate for plenty of plant specimens. This phenomenon has not been extensively studied, due to technical restrictions. This bottleneck now can be overcome by the state-of-the-art three-dimensional (3D) mapping technologies, such as fine-scale terrestrial laser scanning. On these grounds, we proposed to reexamine the basic theories regarding plant forms, and then, we case validated the feasibility of upgrading them into 3D modes. As an in-time enlightening of 3D revolutionizing the related basic subject, our theoretical prospect further sorted out the potential challenges as the cutting points for advancing its future exploration, which may enable 3D reconstruction of the basic theories of plant forms and even boost life science.

Effects of Stem Density on Crown Architecture of Scots Pine Trees.

Trees adapt to their growing conditions by regulating the sizes of their parts and their relationships. For example, removal or death of adjacent trees increases the growing space and the amount of light received by the remaining trees enabling their crowns to expand. Knowledge about the effects of silvicultural practices on crown size and shape and also about the quality of branches affecting the shape of a crown is, however, still limited. Thus, the aim was to study the crown structure of individual Scots pine trees in forest stands with varying stem densities due to past forest management practices. Furthermore, we wanted to understand how crown and stem attributes and also tree growth affect stem area at the height of maximum crown diameter (SAHMC), which could be used as a proxy for tree growth potential. We used terrestrial laser scanning (TLS) to generate attributes characterizing crown size and shape. The results showed that increasing stem density decreased Scots pine crown size. TLS provided more detailed attributes for crown characterization compared with traditional field measurements. Furthermore, decreasing stem density increased SAHMC, and strong relationships (Spearman's correlations > 0.5) were found between SAHMC and crown and stem size and also stem growth. Thus, this study provided quantitative and more comprehensive characterization of Scots pine crowns and their growth potential. The combination of a traditional growth and yield study design and 3D characterization of crown architecture and growth potential can open up new research possibilities.

Performance Assessment of Reference Modelling Methods for Defect Evaluation in Asphalt Concrete.

The deterioration of road conditions and increasing repair deficits pose challenges for the maintenance of reliable road infrastructure, and thus threaten, for example, safety and the fluent flow of traffic. Improved and more efficient procedures for maintenance are required, and these require improved knowledge of road conditions, i.e., improved data. Three-dimensional mapping presents possibilities for large-scale collection of data on road surfaces and automatic evaluation of maintenance needs. However, the development and, specifically, evaluation of large-scale mobile methods requires reliable references. To evaluate possibilities for close-range, static, high-resolution, three-dimensional measurement of road surfaces for reference use, three measurement methods and five instrumentations are investigated: terrestrial laser scanning (TLS, Leica RTC360), photogrammetry using high-resolution professional-grade cameras (Nikon D800 and D810E), photogrammetry using an industrial camera (FLIR Grasshopper GS3-U3-120S6C-C), and structured-light handheld scanners Artec Leo and Faro Freestyle. High-resolution photogrammetry is established as reference based on laboratory measurements and point density. The instrumentations are compared against one another using cross-sections, point-point distances, and ability to obtain key metrics of defects, and a qualitative assessment of the processing procedures for each is carried out. It is found that photogrammetric models provide the highest resolutions (10-50 million points per m2) and photogrammetric and TLS approaches perform robustly in precision with consistent sub-millimeter offsets relative to one another, while handheld scanners perform relatively inconsistently. A discussion on the practical implications of using each of the examined instrumentations is presented.

Utilizing a Terrestrial Laser Scanner for 3D Luminance Measurement of Indoor Environments.

We aim to present a method to measure 3D luminance point clouds by applying the integrated high dynamic range (HDR) panoramic camera system of a terrestrial laser scanning (TLS) instrument for performing luminance measurements simultaneously with laser scanning. We present the luminance calibration of a laser scanner and assess the accuracy, color measurement properties, and dynamic range of luminance measurement achieved in the laboratory environment. In addition, we demonstrate the 3D luminance measuring process through a case study with a luminance-calibrated laser scanner. The presented method can be utilized directly as the luminance data source. A terrestrial laser scanner can be prepared, characterized, and calibrated to apply it to the simultaneous measurement of both geometry and luminance. We discuss the state and limitations of contemporary TLS technology for luminance measuring.

Analysis and Radiometric Calibration for Backscatter Intensity of Hyperspectral LiDAR Caused by Incident Angle Effect.

Hyperspectral LiDAR (HSL) is a new remote sensing detection method with high spatial and spectral information detection ability. In the process of laser scanning, the laser echo intensity is affected by many factors. Therefore, it is necessary to calibrate the backscatter intensity data of HSL. Laser incidence angle is one of the important factors that affect the backscatter intensity of the target. This paper studied the radiometric calibration method of incidence angle effect for HSL. The reflectance of natural surfaces can be simulated as a combination of specular reflection and diffuse reflection. The linear combination of the Lambertian model and Beckmann model provides a comprehensive theory that can be applied to various surface conditions, from glossy to rough surfaces. Therefore, an adaptive threshold radiometric calibration method (Lambertian-Beckmann model) is proposed to solve the problem caused by the incident angle effect. The relationship between backscatter intensity and incident angle of HSL is studied by combining theory with experiments, and the model successfully quantifies the difference between diffuse and specular reflectance coefficients. Compared with the Lambertian model, the proposed model has higher calibration accuracy, and the average improvement rate to the samples in this study was 22.67%. Compared with the results before calibration with the incidence angle of less than 70°, the average improvement rate of the Lambertian-Beckmann model was 62.26%. Moreover, we also found that the green leaves have an obvious specular reflection effect near 650-720 nm, which might be related to the inner microstructure of chlorophyll. The Lambertian-Beckmann model was more helpful to the calibration of leaves in the visible wavelength range. This is a meaningful and a breakthrough exploration for HSL.

Feasibility of Mobile Laser Scanning towards Operational Accurate Road Rut Depth Measurements.

This paper studied the applicability of the Roamer-R4DW mobile laser scanning (MLS) system for road rut depth measurement. The MLS system was developed by the Finnish Geospatial Research Institute (FGI), and consists of two mobile laser scanners and a Global Navigation Satellite System (GNSS)-inertial measurement unit (IMU) positioning system. In the study, a fully automatic algorithm was developed to calculate and analyze the rut depths, and verified in 64 reference pavement plots (1.0 m × 3.5 m). We showed that terrestrial laser scanning (TLS) data is an adequate reference for MLS-based rutting studies. The MLS-derived rut depths based on 64 plots resulted in 1.4 mm random error, which can be considered adequate precision for operational rutting depth measurements. Such data, also covering the area outside the pavement, would be ideal for multiple road environment applications since the same data can also be used in applications, from high-definition maps to autonomous car navigation and digitalization of street environments over time and in space.

A Long-Term Terrestrial Laser Scanning Measurement Station to Continuously Monitor Structural and Phenological Dynamics of Boreal Forest Canopy.

The terrestrial laser scanner (TLS) has become standard technology for vegetation dynamics monitoring. TLS time series have significant underlying application in investigating structural development and dynamics on a daily and seasonal scale. However, the high potential of TLS for the monitoring of long-term temporal phenomena in fully grown trees with high spatial and temporal resolution has not yet been fully explored. Automated TLS platforms for long-term data collection and monitoring of forest dynamics are rare; and long-term TLS time series data is not yet readily available to potential end-user, such as forestry researchers and plant biologists. This work presents an automated and permanent TLS measurement station that collects high frequency and high spatial resolution TLS time series, aiming to monitor short- and long-term phenological changes at a boreal forestry field station (0.006° angular resolution, one scan per hour). The measurement station is the first of its kind considering the scope, accuracy, and length of the time series it produces. The TLS measurement station provides a unique dataset to monitor the 3D physical structure of a boreal forest, enabling new insights into forest dynamics. For instance, the information collected by the TLS station can be used to accurately detect structural changes in tree crowns surrounding the station. These changes and their timing can be linked with the phenological state of plants, such as the start of leaf-out during spring growing season. As the first results of this novel station, we present time series data products collected with the station and what detailed information it provides about the phenological changes in the test site during the leaf sprout in spring.

Improving distribution models of riparian vegetation with mobile laser scanning and hydraulic modelling.

This study aimed at illustrating how direct measurements, mobile laser scanning and hydraulic modelling can be combined to quantify environmental drivers, improve vegetation models and increase our understanding of vegetation patterns in a sub-arctic river valley. Our results indicate that the resultant vegetation models successfully predict riparian vegetation patterns (Rho = 0.8 for total species richness, AUC = 0.97 for distribution) and highlight differences between eight functional species groups (Rho 0.46-0.84; AUC 0.79-0.93; functional group-specific effects). In our study setting, replacing the laser scanning-based and hydraulic modelling-based variables with a proxy variable elevation did not significantly weaken the models. However, using directly measured and modelled variables allows relating species patterns to e.g. stream power or the length of the flood-free period. Substituting these biologically relevant variables with proxies mask important processes and may reduce the transferability of the results into other sites. At the local scale, the amount of litter is a highly important driver of total species richness, distribution and abundance patterns (relative influences 49, 72 and 83%, respectively) and across all functional groups (13-57%; excluding lichen species richness) in the sub-arctic river valley. Moreover, soil organic matter and soil water content shape vegetation patterns (on average 16 and 7%, respectively). Fluvial disturbance is a key limiting factor only for lichen, bryophyte and dwarf shrub species in this environment (on average 37, 6 and 10%, respectively). Fluvial disturbance intensity is the most important component of disturbance for most functional groups while the length of the disturbance-free period is more relevant for lichens. We conclude that striving for as accurate quantifications of environmental drivers as possible may reveal important processes and functional group differences and help anticipate future changes in vegetation. Mobile laser scanning, high-resolution digital elevation models and hydraulic modelling offer useful methodology for improving correlative vegetation models.

Characterizing ecosystem phenological diversity and its macroecology with snow cover phenology.

One critical challenge of exploring flora phenology is on characterizing ecosystem phenological diversity (EPD), and thus how EPD's performance is influenced by climate changes has also been an open macro-ecological question. To fill these two gaps, we proposed an innovative method for reflecting EPD, by taking the advantage of the often-classified inverse factor of spatial resolution discrepancy between the used remote sensing datasets of vegetation phenological dates (green-up and brown-up) and snow cover phenological dates (SPDs) (onset and end) around the Arctic, and further, we examined the cross response/feedbacks of the two kinds of EPDs to the two categories of SPDs. We found that the circumpolar green-up and brown-up EPDs both were shrinking, driven more by the delaying of the onset SPDs than the advancing of the end SPDs; North America and North Eurasia performed with inconsistent EPD response/feedbacks to the related SPD anomalies; and further, the EPD-SPD response/feedbacks in some locations exhibited the time-lag effect, e.g., the green-up EPDs made the strongest response to the onset SPDs of two years earlier. Overall, the validated method and the new findings are of implications for improving the phenology modules in Earth system models, and the contributions of the present study have enlightening significance for kicking off the new EPD branch in macrosystem phenological ecology.

A 10-nm Spectral Resolution Hyperspectral LiDAR System Based on an Acousto-Optic Tunable Filter.

Hyperspectral LiDAR (HSL) technology can obtain spectral and ranging information from targets by processing the recorded waveforms and measuring the time of flight (ToF). With the development of the supercontinuum laser (SCL), it is technically easier to develop an active hyperspectral LiDAR system that can simultaneously collect both spatial information and extensive spectral information from targets. Compared with traditional LiDAR technology, which can only obtain range and intensity information at the selected spectral wavelengths, HSL detection technology has demonstrated its potential and adaptability for various quantitative applications from its spectrally resolved waveforms. However, with most previous HSLs, the collected spectral information is discrete, and such information might be insufficient and restrict the further applicability of the HSLs. In this paper, a tunable HSL technology using an acousto-optic tunable filter (AOTF) as a spectroscopic device was proposed, designed, and tested to address this issue. Both the general range precision and the accuracy of the spectral measurement were evaluated. By tuning the spectroscopic device in the time dimension, the proposed AOTF-HSL could achieve backscattered echo with continuous coverage of the full spectrum of 500-1000 nm, which had the unique characteristics of a continuous spectrum in the visible and near infrared (VNIR) regions with 10 nm spectral resolution. Yellow and green leaves from four plants (aloe, dracaena, balata, and radermachera) were measured using the AOTF-HSL to assess its feasibility in agriculture application. The spectral profiles measured by a standard spectrometer (SVC© HR-1024) were used as a reference for evaluating the measurements of the AOTF-HSL. The difference between the spectral measurements collected from active and passive instruments was minor. The comparison results show that the AOTF-based consecutive and high spectral resolution HSL was effective for this application.

Extrinsic Calibration of 2D Laser Rangefinders Using an Existing Cuboid-Shaped Corridor as the Reference.

Laser rangefinders (LRFs) are widely used in autonomous systems for indoor positioning and mobile mapping through the simultaneous localization and mapping (SLAM) approach. The extrinsic parameters of multiple LRFs need to be determined, and they are one of the key factors impacting system performance. This study presents an extrinsic calibration method of multiple LRFs that requires neither extra calibration sensors nor special artificial reference landmarks. Instead, it uses a naturally existing cuboid-shaped corridor as the calibration reference, and it hence needs no additional cost. The present method takes advantage of two types of geometric constraints for the calibration, which can be found in a common cuboid-shaped corridor. First, the corresponding point cloud is scanned by the set of LRFs. Second, the lines that are scanned on the corridor surfaces are extracted from the point cloud. Then, the lines within the same surface and the lines within two adjacent surfaces satisfy the coplanarity constraint and the orthogonality constraint, respectively. As such, the calibration problem is converted into a nonlinear optimization problem with the constraints. Simulation experiments and experiments based on real data verified the feasibility and stability of the proposed method.

The Accuracy Comparison of Three Simultaneous Localization and Mapping (SLAM)-Based Indoor Mapping Technologies.

The growing interest and the market for indoor Location Based Service (LBS) have been drivers for a huge demand for building data and reconstructing and updating of indoor maps in recent years. The traditional static surveying and mapping methods can't meet the requirements for accuracy, efficiency and productivity in a complicated indoor environment. Utilizing a Simultaneous Localization and Mapping (SLAM)-based mapping system with ranging and/or camera sensors providing point cloud data for the maps is an auspicious alternative to solve such challenges. There are various kinds of implementations with different sensors, for instance LiDAR, depth cameras, event cameras, etc. Due to the different budgets, the hardware investments and the accuracy requirements of indoor maps are diverse. However, limited studies on evaluation of these mapping systems are available to offer a guideline of appropriate hardware selection. In this paper we try to characterize them and provide some extensive references for SLAM or mapping system selection for different applications. Two different indoor scenes (a L shaped corridor and an open style library) were selected to review and compare three different mapping systems, namely: (1) a commercial Matterport system equipped with depth cameras; (2) SLAMMER: a high accuracy small footprint LiDAR with a fusion of hector-slam and graph-slam approaches; and (3) NAVIS: a low-cost large footprint LiDAR with Improved Maximum Likelihood Estimation (IMLE) algorithm developed by the Finnish Geospatial Research Institute (FGI). Firstly, an L shaped corridor (2nd floor of FGI) with approximately 80 m length was selected as the testing field for Matterport testing. Due to the lack of quantitative evaluation of Matterport indoor mapping performance, we attempted to characterize the pros and cons of the system by carrying out six field tests with different settings. The results showed that the mapping trajectory would influence the final mapping results and therefore, there was optimal Matterport configuration for better indoor mapping results. Secondly, a medium-size indoor environment (the FGI open library) was selected for evaluation of the mapping accuracy of these three indoor mapping technologies: SLAMMER, NAVIS and Matterport. Indoor referenced maps were collected with a small footprint Terrestrial Laser Scanner (TLS) and using spherical registration targets. The 2D indoor maps generated by these three mapping technologies were assessed by comparing them with the reference 2D map for accuracy evaluation; two feature selection methods were also utilized for the evaluation: interactive selection and minimum bounding rectangles (MBRs) selection. The mapping RMS errors of SLAMMER, NAVIS and Matterport were 2.0 cm, 3.9 cm and 4.4 cm, respectively, for the interactively selected features, and the corresponding values using MBR features were 1.7 cm, 3.2 cm and 4.7 cm. The corresponding detection rates for the feature points were 100%, 98.9%, 92.3% for the interactive selected features and 100%, 97.3% and 94.7% for the automated processing. The results indicated that the accuracy of all the evaluated systems could generate indoor map at centimeter-level, but also variation of the density and quality of collected point clouds determined the applicability of a system into a specific LBS.

Can Leaf Water Content Be Estimated Using Multispectral Terrestrial Laser Scanning? A Case Study With Norway Spruce Seedlings.

Changing climate is increasing the amount and intensity of forest stress agents, such as drought, pest insects, and pathogens. Leaf water content, measured here in terms of equivalent water thickness (EWT), is an early indicator of tree stress that provides timely information about the health status of forests. Multispectral terrestrial laser scanning (MS-TLS) measures target geometry and reflectance simultaneously, providing spatially explicit reflectance information at several wavelengths. EWT and leaf internal structure affect leaf reflectance in the shortwave infrared region that can be used to predict EWT with MS-TLS. A second wavelength that is sensitive to leaf internal structure but not affected by EWT can be used to normalize leaf internal effects on the shortwave infrared region and improve the prediction of EWT. Here we investigated the relationship between EWT and laser intensity features using multisensor MS-TLS at 690, 905, and 1,550 nm wavelengths with both drought-treated and Endoconidiophora polonica inoculated Norway spruce seedlings to better understand how MS-TLS measurements can explain variation in EWT. In our study, a normalized ratio of two wavelengths at 905 and 1,550 nm and length of seedling explained 91% of the variation (R2) in EWT as the respective prediction accuracy for EWT was 0.003 g/cm2 in greenhouse conditions. The relation between EWT and the normalized ratio of 905 and 1,550 nm wavelengths did not seem sensitive to a decreased point density of the MS-TLS data. Based on our results, different EWTs in Norway spruce seedlings show different spectral responses when measured using MS-TLS. These results can be further used when developing EWT monitoring for improving forest health assessments.

LiDAR Scan Matching Aided Inertial Navigation System in GNSS-Denied Environments.

A new scan that matches an aided Inertial Navigation System (INS) with a low-cost LiDAR is proposed as an alternative to GNSS-based navigation systems in GNSS-degraded or -denied environments such as indoor areas, dense forests, or urban canyons. In these areas, INS-based Dead Reckoning (DR) and Simultaneous Localization and Mapping (SLAM) technologies are normally used to estimate positions as separate tools. However, there are critical implementation problems with each standalone system. The drift errors of velocity, position, and heading angles in an INS will accumulate over time, and on-line calibration is a must for sustaining positioning accuracy. SLAM performance is poor in featureless environments where the matching errors can significantly increase. Each standalone positioning method cannot offer a sustainable navigation solution with acceptable accuracy. This paper integrates two complementary technologies-INS and LiDAR SLAM-into one navigation frame with a loosely coupled Extended Kalman Filter (EKF) to use the advantages and overcome the drawbacks of each system to establish a stable long-term navigation process. Static and dynamic field tests were carried out with a self-developed Unmanned Ground Vehicle (UGV) platform-NAVIS. The results prove that the proposed approach can provide positioning accuracy at the centimetre level for long-term operations, even in a featureless indoor environment.

Fast fingerprint database maintenance for indoor positioning based on UGV SLAM.

Indoor positioning technology has become more and more important in the last two decades. Utilizing Received Signal Strength Indicator (RSSI) fingerprints of Signals of OPportunity (SOP) is a promising alternative navigation solution. However, as the RSSIs vary during operation due to their physical nature and are easily affected by the environmental change, one challenge of the indoor fingerprinting method is maintaining the RSSI fingerprint database in a timely and effective manner. In this paper, a solution for rapidly updating the fingerprint database is presented, based on a self-developed Unmanned Ground Vehicles (UGV) platform NAVIS. Several SOP sensors were installed on NAVIS for collecting indoor fingerprint information, including a digital compass collecting magnetic field intensity, a light sensor collecting light intensity, and a smartphone which collects the access point number and RSSIs of the pre-installed WiFi network. The NAVIS platform generates a map of the indoor environment and collects the SOPs during processing of the mapping, and then the SOP fingerprint database is interpolated and updated in real time. Field tests were carried out to evaluate the effectiveness and efficiency of the proposed method. The results showed that the fingerprint databases can be quickly created and updated with a higher sampling frequency (5Hz) and denser reference points compared with traditional methods, and the indoor map can be generated without prior information. Moreover, environmental changes could also be detected quickly for fingerprint indoor positioning.

Long-term prediction of the Arctic ionospheric TEC based on time-varying periodograms.

Knowledge of the polar ionospheric total electron content (TEC) and its future variations is of scientific and engineering relevance. In this study, a new method is developed to predict Arctic mean TEC on the scale of a solar cycle using previous data covering 14 years. The Arctic TEC is derived from global positioning system measurements using the spherical cap harmonic analysis mapping method. The study indicates that the variability of the Arctic TEC results in highly time-varying periodograms, which are utilized for prediction in the proposed method. The TEC time series is divided into two components of periodic oscillations and the average TEC. The newly developed method of TEC prediction is based on an extrapolation method that requires no input of physical observations of the time interval of prediction, and it is performed in both temporally backward and forward directions by summing the extrapolation of the two components. The backward prediction indicates that the Arctic TEC variability includes a 9 years period for the study duration, in addition to the well-established periods. The long-term prediction has an uncertainty of 4.8-5.6 TECU for different period sets.

NAVIS-An UGV indoor positioning system using laser scan matching for large-area real-time applications.

Laser scan matching with grid-based maps is a promising tool for real-time indoor positioning of mobile Unmanned Ground Vehicles (UGVs). While there are critical implementation problems, such as the ability to estimate the position by sensing the unknown indoor environment with sufficient accuracy and low enough latency for stable vehicle control, further development work is necessary. Unfortunately, most of the existing methods employ heuristics for quick positioning in which numerous accumulated errors easily lead to loss of positioning accuracy. This severely restricts its applications in large areas and over lengthy periods of time. This paper introduces an efficient real-time mobile UGV indoor positioning system for large-area applications using laser scan matching with an improved probabilistically-motivated Maximum Likelihood Estimation (IMLE) algorithm, which is based on a multi-resolution patch-divided grid likelihood map. Compared with traditional methods, the improvements embodied in IMLE include: (a) Iterative Closed Point (ICP) preprocessing, which adaptively decreases the search scope; (b) a totally brute search matching method on multi-resolution map layers, based on the likelihood value between current laser scan and the grid map within refined search scope, adopted to obtain the global optimum position at each scan matching; and (c) a patch-divided likelihood map supporting a large indoor area. A UGV platform called NAVIS was designed, manufactured, and tested based on a low-cost robot integrating a LiDAR and an odometer sensor to verify the IMLE algorithm. A series of experiments based on simulated data and field tests with NAVIS proved that the proposed IMEL algorithm is a better way to perform local scan matching that can offer a quick and stable positioning solution with high accuracy so it can be part of a large area localization/mapping, application. The NAVIS platform can reach an updating rate of 12 Hz in a feature-rich environment and 2 Hz even in a feature-poor environment, respectively. Therefore, it can be utilized in a real-time application.

Possibilities of a personal laser scanning system for forest mapping and ecosystem services.

A professional-quality, personal laser scanning (PLS) system for collecting tree attributes was demonstrated in this paper. The applied system, which is wearable by human operators, consists of a multi-constellation navigation system and an ultra-high-speed phase-shift laser scanner mounted on a rigid baseplate and consisting of a single sensor block. A multipass-corridor-mapping method was developed to process PLS data and a 2,000 m2 forest plot was utilized in the test. The tree stem detection accuracy was 82.6%; the root mean square error (RMSE) of the estimates of tree diameter at breast height (DBH) was 5.06 cm; the RMSE of the estimates of tree location was 0.38 m. The relative RMSE of the DBH estimates was 14.63%. The results showed, for the first time, the potential of the PLS system in mapping large forest plots. Further research on mapping accuracy in various forest conditions, data correction methods and multi-sensoral positioning techniques is needed. The utilization of this system in different applications, such as harvester operations, should also be explored. In addition to collecting tree-level and plot-level data for forest inventory, other possible applications of PLS for forest ecosystem services include mapping of canopy gaps, measuring leaf area index of large areas, documenting and visualizing forest routes feasible for recreation, hiking and berry and mushroom picking.