Self‐supervised learning to visually detect terrain surfaces for autonomous robots operating in forested terrain

Autonomous robotic navigation in forested environments is difficult because of the highly variable appearance and geometric properties of the terrain. In most navigation systems, researchers assume a priori knowledge of the terrain appearance properties, geometric properties, or both. In forest environments, vegetation such as trees, shrubs, and bushes has appearance and geometric properties that vary with change of seasons, vegetation age, and vegetation species. In addition, in forested environments the terrain surface is often rough, sloped, and/or covered with a surface layer of grass, vegetation, or snow. The complexity of the forest environment presents difficult challenges for autonomous navigation systems. In this paper, a self‐supervised sensing approach is introduced that attempts to robustly identify a drivable terrain surface for robots operating in forested terrain. The sensing system employs both LIDAR and vision sensor data. There are three main stages in the system: feature learning, feature training, and terrain prediction. In the feature learning stage, 3D range points from LIDAR are analyzed to obtain an estimate of the ground surface location. In the feature training stage, the ground surface estimate is used to train a visual classifier to discriminate between ground and nonground regions of the image. In the prediction stage, the ground surface location can be estimated at high frequency solely from vision sensor data. © 2012 Wiley Periodicals, Inc.     

Tree Height and Diameter Extraction with 3D Reconstruction in a Forest based on TLS

In view of the low accuracy of Tree Height(TH) and Diameter at Breast Height(DBH) estimation,as well as the difficulty of individual tree modeling in dense forest,a method to extract forest structure parameters(TH and DBH) and reconstruct a Three-Dimensional(3D) model of forest in subtropical environment based on TLS point cloud data is proposed.The first step is to apply a multi-scale method to extract the ground points for the generation of Digital Elevation Model(DEM).Secondly,using similarity of principal direction between neighboring points and distribution density of points,trunk and other plant organs are separated.Next the trunk points are processed to automatically estimate the tree position and DBH by iterative least squares cylinder fitting;the tree height is automatically estimated by using the octree segmentation.Finally,by combining with the technology of individual tree modeling,a plot-scale 3D forest scene has been reconstructed by planting individual tree model on the terrain model iteratively.The results showed that the correlation coefficient of DBH is R²=0.996,and the average relative error was 2.09%,RMSE was 0.66 cm;the correlation coefficient of tree height is R²=0.972,and the average relative error was 2.16% with RMSE of 0.92 m.The plot-scale reconstructed 3D model of the forest can express the true shape of forest.     

An improved topographic mapping technique from airborne lidar: application in a forested hillside

We developed a robust method to reconstruct a digital terrain model (DTM) by classifying raw light detection and ranging (lidar) points into ground and non-ground points with the help of the Progressive Terrain Fragmentation (PTF) method. PTF applies iterative steps for searching terrain points by approximating terrain surfaces using the triangulated irregular network (TIN) model constructed from ground return points. Instead of using absolute slope or offset distance, PTF uses orthogonal distance and relative angle between a triangular plane and a node. Due to this characteristic, PTF was able to classify raw lidar points into ground and non-ground points on a heterogeneous steep forested area with a small number of parameters. We tested this approach by using a lidar data set covering a part of the Angelo Coast Range Reserve on the South Fork of the Eel River in Mendocino County, California, USA. We used systematically positioned 16 reference plots to determine the optimal parameter that can be used to separate ground and non-ground points from raw lidar point clouds. We tested at different admissible hillslope angles (15° to 20°), and the minimum total error (1.6%) was acquired at the angle value of 18°. Because classifying raw lidar points into ground and non-ground points is the basis for other types of analyses, we expect that our study will provide more accurate terrain approximation and contribute to improving the extraction of other forest biophysical parameters.     

Assessing the attributes of high-density Eucalyptus globulus stands using airborne laser scanner data

This article presents an airborne Light Detection and Ranging (LiDAR)-based method to extract interesting stand attributes for forest management in high-density Eucalyptus globulus Labill. plantations. An adaptive morphological filter (AMF) for classifying terrain LiDAR points in forested areas is used to classify LiDAR points; canopy cover (CC), number of LiDAR-detected trees per hectare (N _LD) and individual tree height (h _tree) were calculated using the canopy height model (CHM); and several statistics and metrics extracted from the CHM and the normalized height of the LiDAR data cloud (NHD) were incorporated into the linear and multiplicative models for estimating mean height (H _m), dominant height (H _d), mean diameter (d _m), quadratic mean diameter (d _g), number of stems per hectare (N), basal area (G) and volume (V). The height accuracy results of the LiDAR-derived digital terrain model (DTM), root mean square error (RMSE)?=?0.303 m, revealed that the developed filter behaved well. The values of the RMSE for CC, N _LD and h _tree were 13.2%, 733.3 stems ha^–1 and 1.91 m, respectively. The regressions explained 78% of the variance in ground-truth values for H _m (RMSE?=?1.33 m); 92% for H _d (RMSE?=?1.18 m); 71% for d _m (RMSE?=?1.68 cm); 73% for d _g (RMSE?=?1.66 cm); 49% for N (RMSE?=?667 stems ha^–1); 78% for G (RMSE?=?5.30 m² ha^–1); and 81% for V (RMSE?=?53.6 m³ ha^–1).     

Measuring forest structure with terrestrial laser scanning

This paper investigates the application of a ground‐based laser scanning system for providing quantitative tree measurements in densely stocked plantation forests. A methodology is tested in Kielder Forest, northern England using stands of mature Sitka spruce (Picea sitchensis) and a structured mixture of Sitka spruce and lodgepole pine (Pinus contorta), standing at tree densities of 600 and 2800?stems?ha^?1 respectively. Three laser scans, two in the Sitka spruce and one in structured mixture, were collected using a Reigl Inc. LPM‐300VHS high‐speed laser scanner. Field measurements were recorded at the same time and included tree diameter at breast height (dbh) and tree height. These measurements were then compared with those derived from the scanner. The results demonstrate that accurate measurements of tree diameter can be derived directly from the laser scan point cloud return in instances where the sensor's view of the tree is not obstructed. Measurements of upper stem diameters, branch internodal distance and canopy dimensions can also be measured from the laser scan data. However, at the scanning spatial resolution selected, it was not possible to measure branch size. The level of detail that can be obtained from the scan data is dependent on the number and location of scans within the plot as well as the scanning resolution. Essentially, as the shadowing caused by tree density or branching frequency increases, the amount of useful information contained in the scan decreases.     

Using terrestrial laser scanning to support biomass estimation in densely stocked young tree plantations

The total area of short-rotation tree plantations is increasing globally, one reason being the need to grow sustainable biomass for bio-energy production. Such stands are usually established with a very high stem density, and inventories for biomass estimation require the adaptation of traditional methods. In this study, we tested a novel, efficient, and non-destructive method for biomass estimation relevant to a high-density, short-rotation oak stand of about 16,500 stems ha^?1. We used terrestrial laser scanning (TLS) in a single-scan design to measure diameter at breast height (DBH) of all trees within 2 m-radius sample plots. Allometric models were then used to predict the tree biomass from their diameter. Biomass estimates were compared to the true biomass determined after harvesting of the sample plots. Mean absolute error and mean relative error were 12.9 kg and 16.4%, respectively, and the coefficient of determination of the relationship between traditionally measured and scan-based biomass was r² = 0.65 (p < 0.001). This TLS-based approach is promising as it considerably reduces fieldwork efforts in dense stands compared with traditional diameter tallying by calipers or tapes.     

基于高程归一化的布料模拟滤波算法

点云的滤波处理是LiDAR数据处理中一个非常重要的环节,即分离出点云数据中的地面点和非地面点,为后续的数据处理打下基础。本文在传统的渐进式数学形态学滤波和布料模拟滤波方法的基础上,考虑到渐进形态学滤波对于地面点分离的效果尚可,也就是能基本保留所有的地面点,但由于其地形的自适应性较弱,高差阈值随着地形坡度的变化也有着不稳定性使得一部分非地面点容易被当成地面点,而布料模拟滤波算法具备运行效率高的优点,且布料模拟滤波在地形平坦地区的滤波效果较地形起伏大的地区滤波效果更好。因此在渐进形态学滤波结果的基础上建立目标区域的粗DEM栅格数据,然后对目标区域点云数据中各点的高程值进行一个归一化处理,消除目标区域中地形有起伏的因素给布料模拟滤波结果带来的影响。最后采用ISPRS官方网站的3组标准数据样本的实验结果表明,相比于传统的渐进式形态学滤波的结果其I类误差降低,相比于未进行归一化过程的布料模拟滤波算法的结果其II类误差降低,而其总误差均降低,达到较好的滤波效果。     

Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape

Meso-scale digital terrain models (DTMs) and canopy-height estimates, or digital canopy models (DCMs), are two lidar products that have immense potential for research in tropical rain forest (TRF) ecology and management. In this study, we used a small-footprint lidar sensor (airborne laser scanner, ALS) to estimate sub-canopy elevation and canopy height in an evergreen tropical rain forest. A fully automated, local-minima algorithm was developed to separate lidar ground returns from overlying vegetation returns. We then assessed inverse distance weighted (IDW) and ordinary kriging (OK) geostatistical techniques for the interpolation of a sub-canopy DTM. OK was determined to be a superior interpolation scheme because it smoothed fine-scale variance created by spurious understory heights in the ground-point dataset. The final DTM had a linear correlation of 1.00 and a root-mean-square error (RMSE) of 2.29 m when compared against 3859 well-distributed ground-survey points. In old-growth forests, RMS error on steep slopes was 0.67 m greater than on flat slopes. On flatter slopes, variation in vegetation complexity associated with land use caused highly significant differences in DTM error distribution across the landscape. The highest DTM accuracy observed in this study was 0.58-m RMSE, under flat, open-canopy areas with relatively smooth surfaces. Lidar ground retrieval was complicated by dense, multi-layered evergreen canopy in old-growth forests, causing DTM overestimation that increased RMS error to 1.95 m.A DCM was calculated from the original lidar surface and the interpolated DTM. Individual and plot-scale heights were estimated from DCM metrics and compared to field data measured using similar spatial supports and metrics. For old-growth forest emergent trees and isolated pasture trees greater than 20 m tall, individual tree heights were underestimated and had 3.67- and 2.33-m mean absolute error (MAE), respectively. Linear-regression models explained 51% (4.15-m RMSE) and 95% (2.41-m RMSE) of the variance, respectively. It was determined that improved elevation and field-height estimation in pastures explained why individual pasture trees could be estimated more accurately than old-growth trees. Mean height of tree stems in 32 young agroforestry plantation plots (0.38 to 18.53 m tall) was estimated with a mean absolute error of 0.90 m (r²=0.97; 1.08-m model RMSE) using the mean of lidar returns in the plot. As in other small-footprint lidar studies, plot mean height was underestimated; however, our plot-scale results have stronger linear models for tropical, leaf-on hardwood trees than has been previously reported for temperate-zone conifer and deciduous hardwoods.     

Stream detection for LiDAR digital elevation models from a forested area

Airborne Laser Swath Mapping instrument technology and subsequent algorithm advances have made it possible over the last few years to map the Earth's surface and land cover at unprecedented resolution. The ability of Airborne Laser Swath Mapping technology to densely sample ground elevations beneath forest canopies is particularly important because forested watersheds have traditionally been difficult to study with remote sensing techniques. The extraction of stream networks from digital elevation models (DEMs) plays a fundamental role in modelling local and spatially distributed hydrological processes. Our approach, based on an encoding of mathematical morphological operators, is shown to systematically and accurately extract stream channel locations, forms and incipient incisions in a forested watershed. The accuracy of the method is verified using a set of error measures over simulated terrain and also over real terrain where the site was manually surveyed.     

GEDI地面高程和森林冠层高度的精度评价与影响分析

精确地提取地面高程和植被冠层高度,对于地形地貌、生态学等方面的研究具有重要意义。2018年12月发射的新一代全球生态系统动力学调查雷达（GEDI）为地面高程和植被冠层高度大范围精确提取提供了前所未有的机会。研究旨在利用机载激光雷达数据验证GEDI提取的地面高程和冠层高度精度,并探讨地理定位误差、地形坡度、坡向、植被覆盖度、方位角、采集时间、光束类型和不同森林类型因素对其精度的影响。结果表明：通过校正GEDI数据地理定位误差,可以明显提高其提取的地面高程和冠层高度精度;影响冠层高度提取精度最主要的因素是植被覆盖度,其次是坡度;影响地面高程提取精度的主要因素为坡向、坡度。植被覆盖度大于25%时,数据精度更高;坡度为0°—5°的缓坡地区地面高程和冠层高度精度最高。该研究结果将为GEDI数据筛选与应用提供依据。     

Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions

Laser scanners of small footprint diameter and high sampling density provide possibility to obtain accurate height information on the forest canopy. When applying tree crown segmentation methods, individual single trees can be recognised and tree height as well as crown area can be detected. Detection of suppressed trees from a height model based on laser scanning is difficult; however, it is possible to predict these trees by using theoretical distribution functions. In this study, two different methods are used to predict small trees. In the first method, the parameter prediction method is utilised with the complete Weibull distribution, the parameters of which are predicted with separate parameter prediction models; thus, small trees are determined from the predicted tree height distribution. In the second method, the two-parameter left-truncated Weibull distribution is fitted to the detected tree height distribution.The results are presented by using timber volume and stem density as predicted stand characteristics. The results showed that the root mean square error (RMSE) for the timber volume is about 25% when using only information obtained from laser scanning, whereas the RMSE for the number of stems per ha is about 75%. Predictions for both characteristics are also highly biased and the underestimates are 24% and 62%, respectively. The use of the parameter prediction method to describe small trees improved the accuracy considerably; the RMSE figures for estimates of timber volume and number of stems are 16.0% and 49.2%, respectively. The bias for the estimates is also decreased to 6.3% for timber volume and 8.2% for the number of stems. When a left-truncated height distribution is used to predict the heights of the missing small trees, the RMSEs for the estimates of timber volume and number of stems are 22.5% and 72.7%, respectively. In the case of the timber volume, the reliability figures for both the original laser scanning-based estimates and for the estimates that also contain small trees are comparable to those obtained by conventional compartment-wise Finnish field inventories.     

Effects of different sensors and leaf-on and leaf-off canopy conditions on echo distributions and individual tree properties derived from airborne laser scanning

The objectives of this study were to quantify and analyze differences in laser height and laser intensity distributions of individual trees obtained from airborne laser scanner (ALS) data for different canopy conditions (leaf-on vs. leaf-off) and sensors. It was also assessed how estimated tree height, stem diameter, and tree species were influenced by these differences. The study was based on 412 trees from a boreal forest reserve in Norway. Three different ALS acquisitions were carried out. Leaf-on and leaf-off data were acquired with the Optech ALTM 3100 sensor, and an additional leaf-on dataset was acquired using the Optech ALTM 1233 sensor. Laser echoes located within the vertical projection of the tree crowns were attributed to different echo categories (“first echoes of many”, “single echoes”, “last echoes of many”) and analyzed. The most pronounced changes in laser height distribution from leaf-on to leaf-off were found for the echo categories denoted as “single” and “last echoes of many” where the distributions were shifted towards the ground under leaf-off conditions. The most pronounced change in the intensity distribution was found for “first echoes of many” where the distribution was extremely skewed towards the lower values under leaf-off conditions compared to leaf-on. Furthermore, the echo height and intensity distributions obtained for the two different sensors also differed significantly. Individual tree properties were estimated fairly accurately in all acquisitions with RMSE ranging from 0.76 to 0.84 m for tree height and from 3.10 to 3.17 cm for stem diameter. It was revealed that tree species was an important model term in both and tree height and stem diameter models. A significantly higher overall accuracy of tree species classification was obtained using the leaf-off acquisition (90 vs. 98%) whereas classification accuracy did not differ much between sensors (90 vs. 93%).     

Satellite lidar vs. small footprint airborne lidar: Comparing the accuracy of aboveground biomass estimates and forest structure metrics at footprint level

The use of lidar remote sensing for mapping the spatial distribution of canopy characteristics has the potential to allow an accurate and efficient estimation of tree dimensions and canopy structural properties from local to regional and continental scales. The overall goal of this paper was to compare biomass estimates and height metrics obtained by processing GLAS waveform data and spatially coincident discrete-return airborne lidar data over forest conditions in east Texas. Since biomass estimates are derived from waveform height metrics, we also compared ground elevation measurements and canopy parameters. More specific objectives were to compare the following parameters derived from GLAS and airborne lidar: (1) ground elevations; (2) maximum canopy height; (3) average canopy height; (4) percentiles of canopy height; and (5) above ground biomass. We used the elliptical shape of GLAS footprints to extract canopy height metrics and biomass estimates derived from airborne lidar. Results indicated a very strong correlation for terrain elevations between GLAS and airborne lidar, with an r value of 0.98 and a root mean square error of 0.78 m. GLAS height variables were able to explain 80% of the variance associated with the reference biomass derived from airborne lidar, with an RMSE of 37.7 Mg/ha. Most of the models comparing GLAS and airborne lidar height metrics had R-square values above 0.9.     

Estimating aboveground forest biomass from canopy reflectance model inversion in mountainous terrain

The amount and spatial distribution of aboveground forest biomass (AGB) are required inputs to forest carbon budgets and ecosystem productivity models. Satellite remote sensing offers distinct advantages for large area and multi-temporal applications, however, conventional empirical methods for estimating forest canopy structure and AGB can be difficult in areas of high relief and variable terrain. This paper introduces a new method for obtaining AGB from forest structure estimates using a physically-based canopy reflectance (CR) model inversion approach. A geometric-optical CR model was run in multiple forward mode (MFM) using SPOT-5 imagery to derive forest structure and biomass at Kananaskis, Alberta in the Canadian Rocky Mountains. The approach first estimates tree crown dimensions and stem density for satellite image pixels which are then related to tree biomass and AGB using a crown spheroid surface area approach. MFM estimates of AGB were evaluated for 36 deciduous (trembling aspen) and conifer (lodgepole pine) field validation sites and compared against spectral mixture analysis (SMA) and normalised difference vegetation index (NDVI) biomass predictions from atmospherically and topographically corrected (SCS+C) imagery. MFM provided the lowest error for all validation plots of 31.7 tonnes/hectare (t/ha) versus SMA (32.6 t/ha error) and NDVI (34.7 t/ha) as well as for conifer plots (MFM: 23.0 t/ha; SMA 27.9 t/ha; NDVI 29.7 t/ha) but had higher error than SMA and NDVI for deciduous plots (by 4.5 t/ha and 2.1 t/ha, respectively). The MFM approach was considerably more stable over the full range of biomass values (67 to 243 t/ha) measured in the field. Field plots with biomass > 1 standard deviation from the field mean (over 30% of plots) had biomass estimation errors of 37.9 t/ha using MFM compared with 65.5 t/ha and 67.5 t/ha error from SMA and NDVI, respectively. In addition to providing more accurate overall results and greater stability over the range of biomass values, the MFM approach also provides a suite of other biophysical structural outputs such as density, crown dimensions, LAI, height and sub-pixel scale fractions. Its explicit physical-basis and minimal ground data requirements are also more appropriate for larger area, multi-scene, multi-date applications with variable scene geometry and in high relief terrain. MFM thus warrants consideration for applications in mountainous and other, less complex terrain for purposes such as forest inventory updates, ecological modeling and terrestrial biomass and carbon monitoring studies.     

Estimating the velocity and direction of moving targets using a single optical VHR satellite sensor image

The present contribution demonstrates the feasibility and explores the limits of a new method for estimating the velocity and direction of moving targets using a single, optical, very‐high‐resolution (VHR) satellite sensor dataset. The method is based on the fact that there is a time lag between the data collection of the panchromatic (P) and multi‐spectral (MS) sensors in the same VHR platform. Consequently, it is developed around three main steps: (1) accurate image‐to‐image registration between MS and P images with a sub‐pixel displacement error, (2) precise location of barycentre of targets by mathematical morphology‐based image transforms, and (3) estimation of the ground velocity and direction of the target using the MS‐P spatial displacement, the known time lag, and an image‐to‐ground transformation taking into account the interior and exterior orientation of the sensors and a terrain height reference. An evaluation of the reliability and limits of the proposed method based on the observation of the results regarding manually selected moving and non‐moving targets is included.     

Multi-Purpose Environment Awareness Approach for Single Line Laser Scanner in a Small Rotorcraft UA

The work presents an environment awareness approach for a small rotorcraft unmanned aircraft (UA) which operates at low height using a single line laser scanner which enables a height estimation with a concurrent detection of ground fixed obstacles. The approach is suitable for small UA which are not able to carry complex and heavy 3D laser scanner mountings having additional drives or mirrors. It works without using external reference systems like DGPS. The approach was especially developed for a mission of the “International Micro Air Vehicle Conference” outdoor contest, where it is the aim to fly through a 6x6m artificial gate. The sensor data processing enables the height estimation above ground as well as the detection of obstacles in order to meet the mission’s goal. The height estimation enables a near-ground flight to prevent a collision with a top boundary of the gate, and a terrain following. The obstacle detection senses the pillars of the gate and finds a safe way through the narrow gate passage. The development and optimisation of the mounting and the sensor processing, as well as the validation, was realized under operational conditions with manual remote control (RC) helicopter flights and virtual flights at a simulation environment. The results of the experiments show that with this approach the mission can be fulfilled as a reliable ground estimation and object detection is ensured.     

Estimating stem volume by tree crown area and tree shadow area extracted from pan‐sharpened Quickbird imagery in open Crimean juniper forests

Although open forests represent approximately 30% of the world's forest resources, there is a clear lack of reliable inventory data to allow sustainable management of this valuable resource from semi‐arid areas. This paper demonstrates that the low ground cover of open forest offers a unique opportunity for deriving single tree attributes from high‐resolution satellite imagery, allowing reliable biomass estimation. More particularly, this study investigates the relationship between field‐measured stem volume and tree attributes, including tree crown area and tree shadow area, measured from pan‐sharpened Quickbird imagery with a 0.61 m resolution in a sparse Crimean juniper (Juniperus excelsa M.Bieb.) forest in south‐western Turkey. First tree shadows and crowns were identified and delineated as individual polygons. Both visual delineation and computer‐aided automatic classification methods were tested. After delineation, stem volume as a function of these image‐measured attributes was modelled using linear regression. The statistical analyses indicated that stem volume was correlated with both shadow area and crown area. The best model for stem volume using shadow area resulted in an adjusted R ² = 0.67, with a root mean square error (RMSE) of 12.5%. The model for stem volume using crown area resulted in an adjusted R ² = 0.51, with a RMSE of 15.2%. The results showed that pan‐sharpened Quickbird imagery is suitable for estimating stem volume and may be useful in reducing the time required for obtaining inventory data in open Crimean juniper forests and other similar open forests.     

Accuracy assessment of automatically derived digital elevation models from aster data in mountainous terrain

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) aboard the Terra satellite was designed to generate along‐track stereo images. The data are available at low cost, providing a feasible opportunity for generating digital elevation models (DEMs) in areas where little or no elevation data are yet available. This study evaluates the accuracy of DEMs extracted from ASTER data covering mountainous terrain. For an assessment of the achieved accuracies in the Andean study site, comparisons were made to similar topographical conditions in Switzerland, where reference data were available. All raw DEMs were filtered and interpolated by the post‐processing tools included with PCI Geomatica, the software package used. After carefully checking the DEM quality, further post‐processing was undertaken to eliminate obvious artefacts such as peaks and sinks. Accuracy was tested by comparing the DEMs in the Swiss Alps to three reference models. The achieved results of the generated DEMs are promising, considering the extreme terrain. Given accurate and well‐distributed ground control points (GCPs), it is possible to generate DEMs with a root mean square (RMS) error between 15?m and 20?m in hilly terrain and about 30?m in mountainous terrain. The DEMs are very accurate in nearly flat regions and on smooth slopes with southern expositions: errors are generally within ±10?m in those cases. Larger errors do appear in forested, snow covered or shady areas and at steep cliffs and deep valleys with extreme errors of a few hundred metres. The evaluation showed that the quality of the DEMs is sufficient for enabling atmospheric, topographic and geometric correction to various satellite datasets and for deriving additional products.     

Wheeled vehicles’ velocity updating by navigating on outdoor terrains

This paper addresses the velocity control of wheeled vehicles regarding the terrain features, beyond detection and avoidance of the obstacles as most current works do. Terrain appearance average is used to enable the wheeled vehicle to adapt velocity such that, as speedy as possible, it safely navigates. The vehicle velocity adaptation imitates the human beings’ driving behavior regarding the terrain features: humans use a quick and imprecise estimation of the terrain features but enough to drive–navigate without sliding or falling. A fuzzy neural network sets the vehicle velocity according to average estimations of terrain roughness. The terrain textures are modeled by the principal components that are enough to use pattern recognition for navigation purpose. One set of tests is executed using a small, wheeled robot, which adjusts velocity while navigating on surfaces such as ground, ground with grass, and stones paving. The other tests are done using images of roads of ground, concrete, asphalt, and loose stones, which are video filmed from a real car driven at less than 60 km/h of velocity; by applying the present approach, the required time/distance ratio to smoothly velocity change is granted.     

A two-stage algorithm for ground filtering of airborne laser scanning data

ABSTRACT

A two-stage algorithm is proposed in this article for ground filtering of airborne laser scanning (ALS) data. Input ALS data are initially preprocessed for outliers removal. The first stage removes the non-ground objects from preprocessed ALS data based on the geometrical reasoning, which is applied over piecewise local neighbourhoods around selected points. The second stage retrieved the ground points falsely recognized as non-ground in the first stage using geometrical similarity of ground points in their surroundings. The proposed algorithm was tested and validated comprehensively using complex and heterogeneous landscapes of selected 10 ALS data sets and additional 15 International Society of Photogrammetry and Remote Sensing data samples. The ground points were filtered out in these data sets and data samples at average total error and kappa coefficient of 3.66% and 89.15%, respectively. The proposed algorithm performs satisfactorily in the complex terrain cases such as mixed vegetation and houses on sloping terrain, low vegetation, complex objects, low and small objects, scene border, and discontinuity. The proposed algorithm is straightforward and, consequently, computationally efficient. Thus, it has potential for wider use in industry.