Accurate three-dimensional (3D) data from indoor spaces are of high importance for various applications in construction, indoor navigation and real estate management. Mobile scanning techniques are offering an efficient way to produce point clouds, but with a lower accuracy than the traditional terrestrial laser scanning (TLS). In this paper, we first tackle the problem of how the quality of a point cloud should be rigorously evaluated. Previous evaluations typically operate on some point cloud subset, using a manually-given length scale, which would perhaps describe the ranging precision or the properties of the environment. Instead, the metrics that we propose perform the quality evaluation to the full point cloud and over all of the length scales, revealing the method precision along with some possible problems related to the point clouds, such as outliers, over-completeness and misregistration. The proposed methods are used to evaluate the end product point clouds of some of the latest methods. In detail, point clouds are obtained from five commercial indoor mapping systems, Matterport, NavVis, Zebedee, Stencil and Leica Pegasus: Backpack, and three research prototypes, Aalto VILMA , FGI Slammer and the Würzburg backpack. These are compared against survey-grade TLS point clouds captured from three distinct test sites that each have different properties. Based on the presented experimental findings, we discuss the properties of the proposed metrics and the strengths and weaknesses of the above mapping systems and then suggest directions for future research.

No abstract

No abstract

Background: Lately, terrestrial point clouds have drawn attention as a new data source for in situ forest investigations. So far, terrestrial laser scanning (TLS) has the highest data quality among all terrestrial point cloud data in terms of geometric accuracy and level of detail (IEEE Transact Geosci Remote Sens 53: 5117-5132, 2015). The TLS point clouds processed by automated algorithms can provide certain individual tree parameters at close to required accuracy in practical applications. However, all terrestrial point clouds face a general challenge, which is the occlusions of upper tree crowns. An emerging technology called unmanned-aerial-vehicle (UAV) -borne laser scanning (ULS) potentially combines the strengths of above and under canopy surveys.Results: The performance of ULS are evaluated in 22 sample plots of various forest stand conditions in a boreal forest. The forest parameter estimates are benchmarked through a comparison with state-of-the-art terrestrial mechanisms from both static terrestrial and mobile laser scanning. The results show that in easy forest stand conditions, the performance of ULS point cloud is comparable with the terrestrial solutions. Conclusions: This study gives the first strict evaluation of ULS in situ observations in varied forest conditions. The study also acts as a benchmarking of available active remote sensing techniques for forest in situ mensuration. The results indicate that the current off-the-shelf ULS has an excellent tree height/tops measurement performance. Although the geometrical accuracy of the ULS data, especially at the stem parts, does not yet reach the level of other terrestrial point clouds, the unbeatable high mobility and fast data acquisition make the ULS a very attractive option in forest investigations.

Abstract:In this paper, we demonstrate the feasibility of using the Microsoft Kinect and Google Tango frame-based depth sensors for individual tree stem measurements and reconstruction for the purpose of forest inventory. Conventionally field reference data in forest inventory are collected at tree and sample plot level by means of manual measurements (e.g., a caliper), which are both labor-intensive and time-consuming. In this study, color (i.e., red, green and blue channels, RGB) and range images acquired by a Kinect and Tango systems were processed and used to extract tree diameter measurements for the individual tree stems. For this, 121 reference stem diameter measurements were made with tape and caliper. Kinect-derived tree diameters agreed with tape measurements to a 1.90 cm root-mean-square error (RMSE). The stem curve from the ground to the diameter at breast height agreed with a bias of 0.7 cm and random error of 0.8 cm with respect to the reference trunk. For Tango measurements, the obtained stem diameters matched those from tape measurement with an RMSE of 0.73 cm, having an average bias of 0.3 cm. As highly portable and inexpensive systems, both Kinect and Tango provide an easy way to collect tree stem diameter and stem curve information vital to forest inventory. These inexpensive instruments may in future compete with both terrestrial and mobile laser scanning or conventional fieldwork using calipers or tape. Accuracy is adequate for practical applications in forestry. Measurements made using Kinect and Tango type systems could also be applied in crowdsourcing context.

Detailed modeling of floodplain flows and associated processes requires data on mixed, heterogeneous vegetation at river reach scale, though the collection of vegetation data is typically limited in resolution or lack spatial information. This study investigates physically-based characterization of mixed floodplain vegetation by means of terrestrial laser scanning (TLS). The work aimed at developing an approach for deriving the characteristic reference areas of herbaceous and foliated woody vegetation, and estimating the vertical distribution of woody vegetation. Detailed experimental data on vegetation properties were gathered both in a floodplain site for herbaceous vegetation, and under laboratory conditions for 2-3 m tall trees. The total plant area (Atot) of woody vegetation correlated linearly with the TLS-based voxel count, whereas the Atot of herbaceous vegetation showed a linear correlation with TLS-based vegetation mean height. For woody vegetation, 1 cm voxel size was found suitable for estimating both the Atot and its vertical distribution. A new concept was proposed for deriving Atot for larger areas from the point cloud attributes of small sub-areas. The results indicated that the relationships between the TLS attributes and Atot of the sub-areas can be derived either by mm resolution TLS or by manual vegetation sampling. OPEN ACCESSWater 2015, 7 421

Abstract:A novel approach to evaluating night-time road and street environment lighting conditions through 3D point clouds is presented. The combination of luminance imaging and 3D point cloud acquired with a terrestrial laser scanner was used for analyzing 3D luminance on the road surface. A calculation of the luminance (cd/m 2 ) was based on the RGB output values of a Nikon D800E digital still camera. The camera was calibrated with a reference luminance source. The relative orientation between the luminance images and intensity image of the 3D point cloud was solved in order to integrate the data sets into the same coordinate system. As a result, the 3D model of road environment luminance is illustrated and the ability to exploit the method for evaluating the luminance distribution on the road surface is presented. Furthermore, the limitations and future prospects of the methodology are addressed. The method provides promising results for studying road lighting conditions OPEN ACCESSRemote Sens. 2015, 7 11390in future lighting optimizations. The paper presents the methodology and its experimental application on a road section which consists of five luminaires installed on one side of a two-lane road in Otaniemi, Espoo, Finland.

The ‘hue-heat’ hypothesis states that an environment which has wavelengths predominantly toward the red end of the visual spectrum feels ‘warm’ and one with wavelengths mainly toward the blue end feels ‘cool’. In order to test the hypothesis and to study the impacts of the correlated colour temperature of a light source on thermal sensation and thermal comfort, a study was conducted in a test room illuminated with an Light Emitting Diode (LED) lighting system with an adjustable correlated colour temperature where air temperature, air velocity, and relative humidity were kept constant. The correlated colour temperature of lighting inside the test room was changed gradually while keeping the colour rendering index values greater than 90, an illuminance level of 500 lx, and chromaticity difference (Duv) values within the limits of ±0.005. Sixteen study subjects were exposed to a ‘high room temperature’ (25℃) and a ‘low room temperature’ (20℃) on different days. The subjects were adapted to low correlated colour temperature (2700 K), medium correlated colour temperature (4000 K), and high correlated colour temperature (6200 K) lighting for 10 min and subsequently completed the questionnaire about their thermal comfort and thermal sensation. The results of this survey did not provide support for the hue-heat hypothesis and indicated that people felt thermally more comfortable in an indoor workplace at the correlated colour temperature of 4000 K than at the correlated colour temperature of 2700 K or 6200 K.