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.
Abstract:A harvester enables detailed roundwood data to be collected during harvesting operations by means of the measurement apparatus integrated into its felling head. These data can be used to improve the efficiency of wood procurement and also replace some of the field measurements, and thus provide both less costly and more detailed ground truth for remote sensing based forest inventories. However, the positional accuracy of harvester-collected tree data is not sufficient currently to match the accuracy per individual trees achieved with remote sensing data. The aim in the present study was to test the accuracy of various instruments utilizing global satellite navigation systems (GNSS) in motion under forest canopies of varying densities to enable us to get an understanding of the current state-of-the-art in GNSS-based positioning under forest canopies. Tests were conducted using several different combinations of GNSS and inertial measurement unit (IMU) mounted OPEN ACCESSForests 2015, 6 3219 on an all-terrain vehicle (ATV) "simulating" a moving harvester. The positions of 224 trees along the driving route were measured using a total-station and real-time kinematic GPS. These trees were used as reference items. The position of the ATV was obtained using GNSS and IMU with an accuracy of 0.7 m (root mean squared error (RMSE) for 2D positions). For the single-frequency GNSS receivers, the RMSE of real-time 2D GNSS positions was 4.2-9.3 m. Based on these results, it seems that the accuracy of novel single-frequency GNSS devices is not so dependent on forest conditions, whereas the performance of the tested geodetic dual-frequency receiver is very sensitive to the visibility of the satellites. When postprocessing can be applied, especially when combined with IMU data, the improvement in the accuracy of the dual-frequency receiver was significant.
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%...
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