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3-D classroom seating information is of great significance in a host of planning contexts including indoor navigation and facility management. Mapping classroom 3-D seats based on a single-view LiDAR scan is challenging because of occlusions and viewpoint limitations. In essence, one is left with incomplete point clouds, making object recognition difficult. The aim of this paper is to acquire complete point clouds for objects in a room based on a single-view LiDAR scan, focusing in particular on chairs in indoor spaces such as classrooms, lecture halls, theaters, concert venues, and sports arenas. A spatial optimization enabled framework is proposed for 3-D classroom seat mapping from a single-view LiDAR scan. A two-step clustering method is proposed to segment each individual chair object in the LiDAR scan. A chair model is extracted and used to complete other chair point clouds via registration. Notably, a parameter estimation method is devised based on spatial priors and integrated in an iterative closest point optimization approach to refine registration. Applications to multiple classroom scans demonstrate the effectiveness and accuracy of the proposed methodology. The developed segmentation-registration framework harnesses spatial knowledge to complete a single-view LiDAR scan with many identical objects. The resultant object point cloud is critical for navigation, route guidance, facility management, pandemic response and planning, indoor mapping and venue utilization, among others, which require precise and complete spatial information. Identified 3-D chairs extracted from LiDAR scans provide essential information for stakeholders seating configuration, property management, and indoor navigation. A new and challenging problem of completing partial objects from a single-view LiDAR scan is formulated. The developed framework provides a fast and high-precision solution to process single-view LiDAR scans for extracting and restoring incomplete objects by combining spatial knowledge, machine learning, and spatial optimization. Empirical results demonstrate the capability to acquire complete chair point clouds from multiple classroom scans to support planning and policy efforts in mitigating disease spread. [ABSTRACT FROM AUTHOR]

Efficient three-dimension exploration of a complex indoor environment remains a difficult duration in mapping accuracy requirements for geometric data. Indoor and outdoor mapping, which has been done with classical measuring devices for a long time, has come a long way with the use of laser scanning technology in this field. Although terrestrial laser scanning (TLS) point cloud recording provides great convenience in indoor and outdoor mapping, it is disadvantageous in terms of time and cost. Therefore, in our study, we propose a new indoor mapping strategy with wearable mobile laser scanning (WMLS) approach, which uses the simultaneous localization and mapping algorithm, which has been tried recently, as an alternative to TLS point cloud. In this strategy, four test areas with different geometric structures were scanned. A comparative analysis was performed after each test area was scanned with TLS and WMLS devices. For accuracy analysis, reference data were measured with a precision measuring device, and both scan data were compared on a spatial and length basis. The study results showed that the proposed strategy could be an alternative to TLS in terms of accuracy and the time advantage of the WMLS method. [ABSTRACT FROM AUTHOR]

Due to their low cost and portability, using entertainment devices for indoor mapping applications has become a hot research topic. However, the impact of user behavior on indoor mapping evaluation with entertainment devices is often overlooked in previous studies. This article aims to assess the indoor mapping performance of entertainment devices under different mapping strategies. We chose two entertainment devices, the HoloLens 2 and iPhone 14 Pro, for our evaluation work. Based on our previous mapping experience and user habits, we defined four simplified indoor mapping strategies: straight-forward mapping (SFM), left–right alternating mapping (LRAM), round-trip straight-forward mapping (RT-SFM), and round-trip left–right alternating mapping (RT-LRAM). First, we acquired triangle mesh data under each strategy with the HoloLens 2 and iPhone 14 Pro. Then, we compared the changes in data completeness and accuracy between the different devices and indoor mapping applications. Our findings show that compared to the iPhone 14 Pro, the triangle mesh accuracy acquired by the HoloLens 2 has more stable performance under different strategies. Notably, the triangle mesh data acquired by the HoloLens 2 under the RT-LRAM strategy can effectively compensate for missing wall and floor surfaces, mainly caused by furniture occlusion and the low frame rate of the depth-sensing camera. However, the iPhone 14 Pro is more efficient in terms of mapping completeness and can acquire a complete triangle mesh more quickly than the HoloLens 2. In summary, choosing an entertainment device for indoor mapping requires a combination of specific needs and scenes. If accuracy and stability are important, the HoloLens 2 is more suitable; if efficiency and completeness are important, the iPhone 14 Pro is better. [ABSTRACT FROM AUTHOR]

In global navigation satellite system-denied environments, indoor point-cloud maps are valuable sources of detailed and inaccessible information. Dense visual point clouds can be produced quickly and inexpensively using stereo RGB cameras and simultaneous localization and mapping (SLAM) techniques. However, visual point-cloud mapping is affected by complex visual observation conditions, and a lack of a consistent reference system sets limits to its application. To address these challenges, this study proposes a method that integrates semantic associating associations and reference bridging. This method employs associations between locations, structures, and semantics to mitigate the detrimental effects of light reflections and subpar textures on visual point-cloud reconstruction. Furthermore, a bridge was established to align the original visual point-cloud map with the geographic reference, where the alignment was made based on the bridge’s control points and its trajectory. Experimental results showed that the bridged visual point cloud was consistent with the previous geo-referenced map with the absolute transformation error of 0.53 cm, and the optimized visual point cloud achieved over 90 % in semantic completeness. This approach demonstrates that visual mapping can be more reliable and useful in indoor environments.

Tactile maps of indoor spaces have great potential for supporting pre-journey spatial learning by blind travelers. Due to the limited resolution of tactile sensing, though, only a limited amount of spatial detail can be embossed in a map at a certain scale. We conducted a focus group with blind participants in order to obtain some insight on the perceived utility of using multiple maps at different spatial scales, and thus different level of detail, to represent the interior of a building.

The mobile laser-point cloud measurement system has been widely used in urban highprecision mapping. In some urban spaces with many high-rise buildings and indoor spaces, high-precision point cloud data cannot be directly obtained due to weak or no global navigation satellite system (GNSS) signals. We propose a space-constrained mobile vehicle-mounted laser scanning data acquisition plan that can effectively utilize the advantages of ground 3D laser scanning and mobile vehicle-mounted laser scanning. In addition, the plan adopts multi-source point cloud data fusion technology to obtain high-precision complete point cloud data both indoors and outdoors. The validity and reliability of the plan are verified by data acquisition experiments, which provide a useful reference for future high-precision mapping. [ABSTRACT FROM AUTHOR]

Due to their low cost and portability, using entertainment devices for indoor mapping applications has become a hot research topic. However, the impact of user behavior on indoor mapping evaluation with entertainment devices is often overlooked in previous studies. This article aims to assess the indoor mapping performance of entertainment devices under different mapping strategies. We chose two entertainment devices, the HoloLens 2 and iPhone 14 Pro, for our evaluation work. Based on our previous mapping experience and user habits, we defined four simplified indoor mapping strategies: straight-forward mapping (SFM), left–right alternating mapping (LRAM), round-trip straight-forward mapping (RT-SFM), and round-trip left–right alternating mapping (RT-LRAM). First, we acquired triangle mesh data under each strategy with the HoloLens 2 and iPhone 14 Pro. Then, we compared the changes in data completeness and accuracy between the different devices and indoor mapping applications. Our findings show that compared to the iPhone 14 Pro, the triangle mesh accuracy acquired by the HoloLens 2 has more stable performance under different strategies. Notably, the triangle mesh data acquired by the HoloLens 2 under the RT-LRAM strategy can effectively compensate for missing wall and floor surfaces, mainly caused by furniture occlusion and the low frame rate of the depth-sensing camera. However, the iPhone 14 Pro is more efficient in terms of mapping completeness and can acquire a complete triangle mesh more quickly than the HoloLens 2. In summary, choosing an entertainment device for indoor mapping requires a combination of specific needs and scenes. If accuracy and stability are important, the HoloLens 2 is more suitable; if efficiency and completeness are important, the iPhone 14 Pro is better.

Generating maps of indoor environments beyond the line-of-sight finds applications in several areas such as planning, navigation, and security. While researchers have previously explored the use of RF signals to generate maps, prior work has two important limitations: (i) it requires moving the mapping setup along the entire lengths of the sides of the building, and (ii) it generates maps that are not fully connected, rather are scatter plots of locations from where some obstacles reflected the signals. Thus, prior approaches require human interpretation to locate the walls and determine how they merge. In this article, we address these limitations and propose RFMap, which generates fully connected maps, and does not require the measurement setup to be moved along the sides of the buildings. To generate the map, RFMap first transmits RF signals in many different directions and then measures the distances of different reflectors inside the building. Next, it identifies these reflectors and classifies them into various types based on the properties of the reflections. A key challenge is that RFMap does not receive reflections from all the directions due to the specular nature of the reflectors. Due to this, it only gets sparse data about the objects in the environment. To address this challenge, RFMap trains a deep generative adversarial network (GAN) to intelligently predict the missing information. At runtime, it feeds the locations and types of the detected reflectors to the trained GAN and generates complete and accurate map.We implemented RFMap using software defined radios and extensively evaluated it in several real-world environments. Our results show that RFMap generated the maps of all the buildings that we tested it on with high accuracy. [ABSTRACT FROM AUTHOR]

The aim of the project is to investigate the possibility of using people's mobile phones to locate people in a tunnel environment using the mobile phone's Wi-Fi connection. In total, 39 different trials were carried out under different conditions in a road tunnel in Stockholm, Sweden. In the trials, the Wi-Fi-based predicted location has been compared with the actual location of the recruited 16 participants in the tunnel. The conditions include the number of people in a group, the number of available access points in the tunnel, whether the mobile phone has an active or passive connection, whether a person is moving or standing still and whether the mobile phone is held in the hand or is stored in the person's pocket. The results indicate that the mean value for the distance between actual and predicted locations is in the order of 20 m or less, which is higher than reported in other studies. Despite this, there is a good potential to locate individuals in a tunnel emergency as the distance between emergency exits is often much longer than the uncertainties in the predicted locations of people. Improving the location algorithms will possibly reduce the uncertainty of the predicted location. • Wi-Fi technology can be used to locate people in a road and rail tunnel using people's smart phones. • Location precision is reasonable for the current purpose. • Experimental results are presented as evidence for evaluation of the technology. • Experiments compare real location using video recordings and Wi-Fi-predicted locations. • Better location algorithms have been suggested as one strategy to improve precision. [ABSTRACT FROM AUTHOR]

Integrating efficient connectivity, positioning and sensing functionalities into 5G New Radio (NR) and beyond mobile cellular systems is one timely research paradigm, especially at mm-wave and sub-THz bands. In this article, we address the radio-based sensing and environment mapping prospects with specific emphasis on the user equipment (UE) side. We first describe an efficient $\ell _1$ -regularized least-squares (LS) approach to obtain sparse range–angle charts at individual measurement or sensing locations. For the subsequent environment mapping, we then introduce a novel state model for mapping diffuse and specular scattering, which allows efficient tracking of individual scatterers over time using interacting multiple model (IMM) extended Kalman filter and smoother. Also the related measurement selection and data association problems are addressed. We provide extensive numerical indoor mapping results at the 28 GHz band deploying OFDM-based 5G NR uplink waveform with 400 MHz channel bandwidth, covering both accurate ray-tracing based as well as actual RF measurement results. The results illustrate the superiority of the dynamic tracking-based solutions, compared to static reference methods, while overall demonstrate the excellent prospects of radio-based mobile environment sensing and mapping in future mm-wave networks. [ABSTRACT FROM AUTHOR]

This article presents RoomSLAM, a Simultaneous Localization and Mapping (SLAM) method for mobile robots in indoor environments where environments are modeled by points and quadrilaterals in 2D space. Points represent positions of semantic objects whereas quadrilaterals approximate the structural layout of the environment, namely rooms. The benefit of such modeling is threefold. Firstly, rooms are a logical way to partition a graph in large-scale SLAM. Secondly, rooms and objects reduce search space in data association. Lastly, the model contains a higher level of semantic, which is beneficial to autonomous robots whenever inter-room navigation is needed. The method was evaluated with two public datasets and the results were compared to those of ORBSLAM and RGBDSLAM.

Indoor map is a fundamental element of indoor location-based services (ILBS). However, traditional indoor mapping techniques are labor-intensive and time-consuming. The advancement of smartphones offers great opportunities for crowdsourcing-based indoor mapping, which is one of the most promising applications due to its low cost and flexibility. Over the last decade, many crowdsourcing-based indoor mapping solutions using smartphones have been proposed. This article provides a systematic review of these works. Different from former surveys, we classify the indoor mapping process by the stage of map construction. In particular, we highlight the two key steps, geospatial-element acquisition, and indoor-map construction, and provide state-of-the-art techniques on these topics. Then, we systematically review the crowdsourcing-based indoor mapping solutions under grid-based, landmark-based, and semantic maps. In addition to covering the principles, benefits, and challenges, these systems are compared in terms of sensors, participation, output, experimental environment, and reported accuracy. Besides these existing performance criteria, we extract quantitative performance criteria that are suitable to evaluate crowdsourcing-based indoor mapping solutions. Finally, we present open issues and future research directions. [ABSTRACT FROM AUTHOR]

Total and automatic digitalization of indoor spaces in 3D implies a great advance in building maintenance and construction tasks, which currently require visits and manual works. Terrestrial laser scanners (TLS) have been widely used for these tasks, although the acquisition methodology with TLS systems is time consuming, and each point cloud is acquired in a different coordinate system, so the user has to post-process the data to clean and get a unique point cloud of the whole scenario. This paper presents a solution for the automatic data acquisition and registration of point clouds from indoor scenes, designed for point clouds acquired with a terrestrial laser scanner (TLS) mounted on an unmanned ground vehicle (UGV). The methodology developed allows the generation of one complete dense 3D point cloud consisting of the acquired point clouds registered in the same coordinate system, reaching an accuracy below 1 cm in section dimensions and below 1.5 cm in walls thickness, which makes it valid for quality control in building works. Two different study cases corresponding to building works were chosen for the validation of the method, showing the applicability of the methodology developed for tasks related to the control of the evolution of the construction. [ABSTRACT FROM AUTHOR]

Indoor mapping is an essential element of indoor navigation systems. In this paper, we present a graph optimization-based method for indoor map construction, which can be used in buildings without prior knowledge. By using crowdsourcing data on mobile phone sensors, we can derive activity landmarks where people perform different activities (turning, taking the elevator, taking the escalator, and walking up/down the stairs). Our method uses graph optimization techniques to align crowdsourcing trajectories by their intersecting landmarks. After trajectory alignment, this method applies a pose graph optimization method to construct an indoor map. Finally, our method performs a transform from relative coordinates to an absolute coordinate with reference points and reduces the redundant segments using dynamic time warping. To evaluate the performance, we implement the proposed method in an office building and a shopping mall. Experiment results show that 80th percentile error of the mapping accuracy is about 1.7-3.5 m. Moreover, the proposed method can deal with the curved routes in a building and can also decrease the amount of required data.

Indoor and outdoor mapping studies can be completed relatively quickly, depending on the developments in Mobile Mapping Systems. Especially in indoor environments where high accuracy GNSS positions cannot be used, mapping studies can be carried out with SLAM algorithms. Although there are many different SLAM algorithms in the literature, each can produce results with different accuracy according to the mapped environment. In this study, 3D maps were produced with LOAM, A-LOAM, and HDL Graph SLAM algorithms in different environments such as long corridors, staircases, and outdoor environments, and the accuracies of the maps produced with different algorithms were compared. For this purpose, a mobile mapping platform using Velodyne VLP-16 LIDAR sensor was developed, and the odometer drift, which causes loss of accuracy in the data collected, was minimized by loop closure and plane detection methods. As a result of the tests, it was determined that the results of the LOAM algorithm were not as accurate as those of the A-LOAM and HDL Graph SLAM algorithms. Both indoor and outdoor environments and the A-LOAM results’ accuracy were two times better than HDL Graph SLAM results.

Rapid detection of 3D objects in indoor environments is essential for indoor mapping and modeling, robotic perception and localization, and building reconstruction. 3D point clouds acquired by a low-cost RGB-D camera have become one of the most commonly used data sources for 3D indoor mapping. However, due to the sparse surface, empty object center, and various scales of point cloud objects, 3D bounding boxes are challenging to be estimated and located accurately. To address this, geometric shape, topological structure, and object relation are commonly employed to extract box reasoning information. In this paper, we describe the geometric feature among object points as an intra-object feature and the relation feature between different objects as an inter-object feature. Based on these two features, we propose an end-to-end point cloud geometric relation network focusing on 3D object detection, which is termed as geometric relation network (GRNet). GRNet first extracts intra-object and inter-object features for each representative point using our proposed backbone network. Then, a centralization module with a scalable loss function is proposed to centralize each representative object point to its center. Next, proposal points are sampled from these shifted points, following a proposal feature pooling operation. Finally, an object-relation learning module is applied to predict bounding box parameters. Such parameters are the additive sum of prediction results from the relation-based inter-object feature and the aggregated intra-object feature. Our model achieves state-of-the-art 3D detection results with 59.1% mAP@0.25 and 39.1% mAP@0.5 on ScanNetV2 dataset, 58.4% mAP@0.25 and 34.9% mAP@0.5 on SUN RGB-D dataset. [ABSTRACT FROM AUTHOR]

One of the most important required ability of a mobile robot is perception. An autonomous mobile robot has to be able to gather information from the environment and use it for supporting the accomplishing task. One kind of sensor that essential for this process is distance sensor. This sensor can be used for obtaining the distance of any objects surrounding the robot and utilize the information for localizing, mapping, avoiding obstacles or collisions and many others. In this paper, some of the distance sensor, including Kinect, Hokuyo UTM-30LX, and RPLidar were observed experimentally. Strengths and weaknesses of each sensor were reviewed so that it can be used as a reference for selecting a suitable sensor for any particular application. A software application has been developed in C programming language as a platform for gathering information for all tested sensors. According to the experiment results, it showed that Hokuyo UTM-30LX results in random normally distributed error on measuring distance with average error 21.94 mm and variance 32.11. On the other hand, error measurement resulted by Kinect and RPLidar strongly depended on measured distance of the object from the sensors, while measurement error resulted by Kinect had a negative correlation with the measured distance and the error resulted by RPLidar sensor had a positive correlation with the measured distance. The performance of these three sensors for detecting a transparent object shows that the Kinect sensors can detect the transparent object on its effective range measurement, Hokuyo UTM-30LX can detect the transparent object in the distance more than equal to 200 mm, and the RPLidar sensor cannot detect the transparent object at all tested distance. Lastly, the experiment shows that the Hokuyo UTM-30LX has the fastest processing time significantly, and the RPLidar has the slowest processing time significantly, while the processing time of Kinect sensor was in between. These processing times were not significantly affected by various tested distance measurement.

Due to their great potential for a variety of applications, digital building models are well established in all phases of building projects. Older stock buildings however frequently lack digital representations, and creating these manually is a tedious and time-consuming endeavor. For this reason, the automated reconstruction of building models from indoor mapping data has arisen as an active field of research. In this context, many approaches rely on simplifying suppositions about the structure of buildings to be reconstructed such as, e.g., the well-known Manhattan World assumption. This however not only presupposes that a given building structure itself is compliant with this assumption, but also that the respective indoor mapping dataset is aligned with the coordinate axes. Indoor mapping systems, on the other hand, typically initialize the coordinate system arbitrarily by the sensor pose at the beginning of the mapping process. Thus, indoor mapping data need to be transformed from the local coordinate system, resulting from the mapping process, to a local coordinate system where the coordinate axes are aligned with the Manhattan World structure of the building. This necessary preprocessing step for many indoor reconstruction approaches is also frequently known as pose normalization. In this paper, we present a novel pose-normalization method for indoor mapping point clouds and triangle meshes that is robust against large portions of the indoor mapping geometries deviating from an ideal Manhattan World structure. In the case of building structures that contain multiple Manhattan World systems, the dominant Manhattan World structure supported by the largest fraction of geometries was determined and used for alignment. In a first step, a vertical alignment orienting a chosen axis to be orthogonal to horizontal floor and ceiling surfaces was conducted. Subsequently, a rotation around the resulting vertical axis was determined that aligned the dataset horizontally with the axes of the local coordinate system. The performance of the proposed method was evaluated quantitatively on several publicly available indoor mapping datasets of different complexity. The achieved results clearly revealed that our method is able to consistently produce correct poses for the considered datasets for different input rotations with high accuracy. The implementation of our method along with the code for reproducing the evaluation is made available to the public.

Indoor structures are composed of ceilings, walls and floors that need to be modeled for a variety of applications. This paper proposes an approach to reconstructing models of indoor structures in complex environments. First, semantic pre-processing, including segmentation and occlusion construction, is applied to segment the input point clouds to generate semantic patches of structural primitives with uniform density. Then, a primitives extraction method with detected boundary is introduced to approximate both the mathematical surface and the boundary of the patches. Finally, a constraint-based model reconstruction is applied to achieve the final topologically consistent structural model. Under this framework, both the geometric and structural constraints are considered in a holistic manner to assure topologic regularity. Experiments were carried out with both synthetic and real-world datasets. The accuracy of the proposed method achieved an overall reconstruction quality of approximately 4.60 cm of root mean square error (RMSE) and 94.10% Intersection over Union (IoU) of the input point cloud. The development can be applied for structural reconstruction of various complex indoor environments.

This work presents a concept of intelligent vision-less micro-drones, which are motivated by flying animals such as insects, birds, and bats. The presented micro-drone (named BAT: Blind Autonomous Tiny-drone) can perform bio-inspired complex tasks without the use of cameras. The BAT uses LIDARs and self-emitted optical-flow in order to perform obstacle avoiding and maze-solving. The controlling algorithms were implemented on an onboard micro-controller, allowing the BAT to be fully autonomous. We further present a method for using the information collected by the drone to generate a detailed mapping of the environment. A complete model of the BAT was implemented and tested using several scenarios both in simulation and field experiments, in which it was able to explore and map complex building autonomously even in total darkness.

In this paper, we propose a new hotspot ranking-based indoor mapping and mobility analysis approach based on the sporadically collected crowdsourced Wi-Fi received signal strength (RSS) data. This approach aims to construct the indoor mapping, as well as achieve the mobility analysis of the users following their daily motion patterns in target environment. First, we perform the wavelet analysis with respect to each RSS sequence to mitigate the noise interference to some extent. Second, we develop a new multidimensional scaling approach to map each RSS data into a linear one in the 2-D signal space, which is followed by the density clustering approach to merge the linear ones into different clusters based on the spatial correlation property. Finally, we construct the indoor mapping from the signal into physical spaces by the concept of hotspot ranking order, as well as the transfer relations among different RSS clusters and different physical sub-areas. The experimental results demonstrate that the proposed approach can achieve the superior performance in terms of indoor mapping and mobility analysis in an unknown indoor environment.

Mapping and navigation applications are now considered popular services for mobile phones users. However, despite the fact that people spend most of their time indoors, these applications are still limited to indoor spaces due to the lack of large-scale indoor floorplan databases. In this paper, we present JustWalk: a crowd intelligence-based system for the automatic construction of buildings floorplans. JustWalk employs a participatory sensing approach using smartphones that are ubiquitously available with users who visit a building to automatically and transparently construct accurate motion traces. These accurate traces are generated by reducing the errors in the inertial motion traces by using the points of interest in the indoor environment (e.g., elevators and stairs, etc.) for error resetting. The collected traces are then processed by means of different mathematical and image processing techniques to detect the overall floorplan shape as well as higher level semantics such as detecting rooms and corridors shapes along with a variety of points of interest in the environment. In comparison to other approaches, our system depends only on the users walking patterns and motion traces. It does not require any explicit inputs or obtrusive actions (e.g., taking photos). Experimental evaluation of JustWalk using different android phones in three testbeds shows that it achieves high accuracy for the point of interest detection (0.6 percent false positive and 1 percent false negative rates). In addition, the proposed error resetting technique leads to more than 12 times enhancement in the median distance error compared to the state-of-the-art. Moreover, the detailed floorplan can be accurately estimated with a relatively small number of traces. This number is amortized over the number of users visiting the building. Finally, we show that JustWalk has a small energy footprint on cell-phones, and could be generalized to other buildings; highlighting its promise as a ubiquitous indoor mapping service. [ABSTRACT FROM AUTHOR]

In standard point-based methods, the depth measurements of the point features suffer from noise, which will lead to incorrect global structure of the environment. This paper presents a submap joining based SLAM with an RGB-D camera by introducing planes as well as points as features.This work is consisted of two steps: submap building and submap joining. Several adjacent keyframes, with the corresponding small patches, visual feature points, and planes observed from these keyframes, are used to build a submap. We fuse the submaps into a global map in a sequential fashion, such that, the global structure is recovered gradually through plane feature associations and optimization. We also show that the proposed algorithm can handle plane association problem incrementally in submap level, as the plane covariance can be obtained in each submap. The use of submap significantly reduces the computational cost during the optimization process, while keeping all information about planes. The proposed method is validated using both publicly available RGB-D benchmarks and datasets collected by authors. The algorithm can produce accurate trajectories and high quality 3D models on these challenging datasets, which are difficult for existing RGB-D SLAM or SFM algorithms. • The proposed algorithm takes advantage of planes in structured indoor scenes, thus can be used to build a high-quality 3D model. • The algorithm is very efficient as it applied submap joining technique, which restricts the most computation extensive part within each local submap. • A novel and effective plane parameterization is formulated on manifold showing better convergence properties in the experiments. • The submap based plane association provides a novel way of associate planes in an incremental fashion. [ABSTRACT FROM AUTHOR]

Total and automatic digitalization of indoor spaces in 3D implies a great advance in building maintenance and construction tasks, which currently require visits and manual works. Terrestrial laser scanners (TLS) have been widely used for these tasks, although the acquisition methodology with TLS systems is time consuming, and each point cloud is acquired in a different coordinate system, so the user has to post-process the data to clean and get a unique point cloud of the whole scenario. This paper presents a solution for the automatic data acquisition and registration of point clouds from indoor scenes, designed for point clouds acquired with a terrestrial laser scanner (TLS) mounted on an unmanned ground vehicle (UGV). The methodology developed allows the generation of one complete dense 3D point cloud consisting of the acquired point clouds registered in the same coordinate system, reaching an accuracy below 1 cm in section dimensions and below 1.5 cm in walls thickness, which makes it valid for quality control in building works. Two different study cases corresponding to building works were chosen for the validation of the method, showing the applicability of the methodology developed for tasks related to the control of the evolution of the construction.

The cadastral system provides land ownership information by registering and representing land boundaries on a map. The current cadastral system in Korea, however, focuses mainly on the management of 2D land-surface boundaries. It is not yet possible to provide efficient or reliable land administration, as this 2D system cannot support or manage land information on 3D properties (including architectures and civil infrastructures) for both above-ground and underground facilities. A geometrical model of the 3D parcel, therefore, is required for registration of 3D properties. This paper, considering the role of the cadastral system, proposes a framework for a 3D underground cadastral system that can register various types of 3D underground properties using indoor mapping for as-built Building Information Modeling (BIM). The implementation consists of four phases: (1) geometric modeling of a real underground infrastructure using terrestrial laser scanning data; (2) implementation of as-built BIM based on geometric modeling results; (3) accuracy assessment for created as-built BIM using reference points acquired by total station; and (4) creation of three types of 3D underground cadastral map to represent underground properties. The experimental results, based on indoor mapping for as-built BIM, show that the proposed framework for a 3D underground cadastral system is able to register the rights, responsibilities, and restrictions corresponding to the 3D underground properties. In this way, clearly identifying the underground physical situation enables more reliable and effective decision-making in all aspects of the national land administration system.

Technologies and techniques of location and navigation are advancing, allowing greater precision in locating people in complex and challenging conditions. These advances have attracted growing interest from the scientific community in using indoor positioning systems (IPSs) with a higher degree of precision and fast delivery time, for groups of people such as the visually impaired, to some extent improving their quality of life. Much research brings together various works that deal with the physical and logical approaches of IPSs to give the reader a more general view of the models. These surveys, however, need to be continuously revisited to update the literature on the features described. This paper presents an expansion of the range of technologies and methodologies for assisting the visually impaired in previous works, providing readers and researchers with a more recent version of what was done and the advantages and disadvantages of each approach to guide reviews and discussions about these topics. Finally, we discuss a series of considerations and future trends for the construction of indoor navigation and location systems for the visually impaired.

The Microsoft HoloLens is a head-worn mobile augmented reality device that is capable of mapping its direct environment in real-time as triangle meshes and localize itself within these three-dimensional meshes simultaneously. The device is equipped with a variety of sensors including four tracking cameras and a time-of-flight (ToF) range camera. Sensor images and their poses estimated by the built-in tracking system can be accessed by the user. This makes the HoloLens potentially interesting as an indoor mapping device. In this paper, we introduce the different sensors of the device and evaluate the complete system in respect of the task of mapping indoor environments. The overall quality of such a system depends mainly on the quality of the depth sensor together with its associated pose derived from the tracking system. For this purpose, we first evaluate the performance of the HoloLens depth sensor and its tracking system separately. Finally, we evaluate the overall system regarding its capability for mapping multi-room environments.

With the quick development of RFID technology and the decreasing prices of RFID devices, RFID is becoming widely used in various intelligent services. Especially in the retail application domain, RFID is increasingly adopted to capture the shopping tracks and behavior of in-store customers. To further enhance the potential of this promising application, in this paper, we propose a unified framework for RFID-based path analytics, which uses both in-store shopping paths and RFID-based purchasing data to mine actionable navigation patterns. Four modules of this framework are discussed, which are: (1) mapping from the physical space to the cyber space, (2) data preprocessing, (3) pattern mining and (4) knowledge understanding and utilization. In the data preprocessing module, the critical problem of how to capture the mainstream shopping path sequences while wiping out unnecessary redundant and repeated details is addressed in detail. To solve this problem, two types of redundant patterns, i.e., loop repeat pattern and palindrome-contained pattern are recognized and the corresponding processing algorithms are proposed. The experimental results show that the redundant pattern filtering functions are effective and scalable. Overall, this work builds a bridge between indoor positioning and advanced data mining technologies, and provides a feasible way to study customers’ shopping behaviors via multi-source RFID data.

Beamsteering massive arrays have been recently proposed for indoor environment mapping in next 5G scenarios, thanks to their capability to better penetrate materials with respect to current laser or vision-based systems. From the perspective of integrating radars in small portable devices, architectures based on non-coherent processing of raw measurements represent a viable solution to overcome the limitations of current indoor radio mapping techniques, which entail a too high processing or receiver complexity. In this correspondence, we investigate the capability of low-complexity mobile radars, equipped with mm-wave massive arrays, to adapt to the environment in order to reconstruct it, by adjusting a threshold with respect to the collected data and the radiation pattern. Results, corroborated by means of a measurement campaign, show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Indoor 3D mapping provides a useful three-dimensional structure via an indoor map for many applications. To acquire highly efficient and relatively accurate mapping for large-scale GPS/GNSS-denied scene, we present an upgraded backpacked laser scanning system and a car-mounted indoor mobile laser scanning system. The systems provide both 3D laser scanning point cloud and camera images. In this paper, a simultaneous extrinsic calibration approach for multiple multi-beam LIDAR and multiple cameras is also proposed using the Simultaneous Localization and Mapping (SLAM)-based algorithm. The proposed approach uses the SLAM-based algorithm to achieve a large calibration scene using mobile platforms, registers an acquired multi-beam LIDAR point cloud to the terrestrial LIDAR point cloud to acquire denser points for corner feature extraction, and finally achieves simultaneous calibration. With the proposed mapping and calibration algorithms, we can provide centimetre-lever coloured point cloud with relatively high efficiency and accuracy. [ABSTRACT FROM AUTHOR]

The Worldwide spread of relatively low cost mobile devices embedded with dual rear cameras enables the possibility of exploiting smartphone stereo vision for producing 3D models. Despite such idea is quite attractive, the small baseline between the two cameras restricts the depth discrimination ability of this kind of stereo vision systems. This paper presents the results obtained with a smartphone stereo vision system by using two rear cameras with different focal length: this operating condition clearly reduces the matchable area. Nevertheless, 3D reconstruction is still possible and the obtained results are evaluated for several camera-object distances. [ABSTRACT FROM AUTHOR]

One major challenge in creating indoor maps involves defining their levels of detail or LODs. While a consensus has emerged that indoor maps have at least two types of LODs, semantic and geometric, questions remain regarding their nature, their partitioning, and their relationships with each other as well as with other forms of LOD. Since semantics deals with the meanings of things, semantic LODs (SLODs) deal with the definition, classification, and partitioning of mapped entities. Unlike geometric LODs that are amenable to automation, SLODs have a more qualitative nature that defies automation and requires the careful application of human judgment. This paper proposes a framework for organizing semantic LODs by first classifying them based on the tangibility of mapped entities (i.e., intangible open spaces versus tangible physical features comprised of building structures and equipment and furnishings) and then partitioning each class based on the idea of permanence, defined here as an entity's tendency to remain stationary over time. A cartographic process for integrating SLODs with geometric and appearance LODs is also introduced along with several examples. [ABSTRACT FROM AUTHOR]

In recent years, growing public interest in three-dimensional technology has led to the emergence of affordable platforms that can capture 3D scenes for use in a wide range of consumer applications. These platforms are often widely available, inexpensive, and can potentially find dual use in taking measurements of indoor spaces for creating indoor maps. Their affordability, however, usually comes at the cost of reduced accuracy and precision, which becomes more apparent when these instruments are pushed to their limits to scan an entire room. The point cloud measurements they produce often exhibit systematic drift and random noise that can make performing comparisons with accurate data difficult, akin to trying to compare a fuzzy trapezoid to a perfect square with sharp edges. This paper outlines a process for assessing the accuracy and precision of these imperfect point clouds in the context of indoor mapping by integrating techniques such as the extended Gaussian image, iterative closest point registration, and histogram thresholding. A case study is provided at the end to demonstrate use of this process for evaluating the performance of the Scanse Sweep 3D, an ultra-low cost panoramic laser scanner. [ABSTRACT FROM AUTHOR]

Accurate indoor 3D models have become a key prerequisite for various applications. Through state-of-the-art image processing techniques, 3D models can be generated from high quality images captured by off-the-shelf digital cameras. To acquire redundant data and produce real scale models, a multi-camera system can be used. However, dedicated approaches for image-based 3D reconstruction using mapping platforms equipped with multiple cameras have not been fully addressed. Assuming the availability of prior information regarding the platform trajectory, this paper presents a new approach for reliable estimation of system motion parameters between different data acquisition epochs of a multi-camera system. This approach, which assumes planar motion of the utilized platform, provides a three-point closed-form solution. The derived solutions are then incorporated within a modified RANSAC framework for outlier detection/removal. It is worth noting that, different from the existing General Camera Model (GCM)-based solutions, the proposed approach is based on a modified co-planarity model, which is essentially a direct extension of the classic stereo-based relative orientation. Moreover, since the proposed approach only provides a maximum number of four possible solutions for system motion parameters over different epochs, it has better computational efficiency when compared to other existing algorithms. Experimental results from real datasets acquired with different configurations have demonstrated the reliability of the proposed approach in motion parameter estimation for indoor multi-camera mapping systems. [ABSTRACT FROM AUTHOR]

Abstract: By combining game engines with indoor mapping, it is possible to create interactive virtual environments that represent the real world. In this paper a virtual version of an audio installation in a historic building is produced, where the user freely explores the building and experiences a set of audio clips, creating a virtual radio play. A depth camera indoor mapping system, Matterport, captured a set of staged interiors. The aim was to evaluate the quality and usability of the indoor models and to demonstrate applying them in 3D application development. The quality of the models was evaluated by comparison with laser scanning, revealing limitations with Matterport: increasing the size of the measured area lowered the level of detail and accuracy of volumetric models. The quality of textures was not affected by this limitation, maintaining the appearance of models. To obtain optimised models for mobile 3D applications, a manual revision process was applied. [ABSTRACT FROM AUTHOR]

This paper presents a Wearable Prototype for indoor mapping developed by the University of Vigo. The system is based on a Velodyne LiDAR, acquiring points with 16 rays for a simplistic or low-density 3D representation of reality. With this, a Simultaneous Localization and Mapping (3D-SLAM) method is developed for the mapping and generation of 3D point clouds of scenarios deprived from GNSS signal. The quality of the system presented is validated through the comparison with a commercial indoor mapping system, Zeb-Revo, from the company GeoSLAM and with a terrestrial LiDAR, Faro Focus3D X330. The first is considered as a relative reference with other mobile systems and is chosen due to its use of the same principle for mapping: SLAM techniques based on Robot Operating System (ROS), while the second is taken as ground-truth for the determination of the final accuracy of the system regarding reality. Results show that the accuracy of the system is mainly determined by the accuracy of the sensor, with little increment in the error introduced by the mapping algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we present our novel approach for the crowdsourced dynamic vertical mapping of buildings. For achieving this, we use the barometric sensor of smartphones to estimate altitude differences and the moment of the outdoor to indoor transition to extract reference pressure. We have identified the outdoor–indoor transition (OITransition) via the fusion of four different sensors. Our approach has been evaluated extensively over a period of 6 months in different humidity, temperature, and cloud-coverage situations, as well as over different hours of the day, and it is found that it can always predict the correct number of floors, while it can approximate the altitude with an average error of 0.5 m. [ABSTRACT FROM AUTHOR]

MLS (Mobile Laser Scanner) offers adequate mobility and the ability to obtain accurate environmental data. However, it still faces some challenges, such as the heavy reliance on high-resolution LiDAR and the poor reliability of the mapping process, which limit its effectiveness in engineering projects. Therefore, we propose a novel, automatic and efficient indoor mapping method that utilizes low-cost MLS point clouds and architectural skeleton constraints. First, we determine the initial localization using designed architectural skeleton feature patterns and descriptors. Second, we register adjacent scans with different registration rules due to different abundance of architectural skeletons. Third, we correct cumulative error for both loop and non-loop areas. Last, we stitch scans into the indoor point cloud map, and build the novel lightweight point cloud map. Experiments are carried out in three typical large-scale scenes inside buildings, and results show that our method can produce accurate indoor maps automatically and efficiently. • Automatic indoor mapping via low-cost MLS in large-scale complex indoor scenes. • Usage of various available architectural reference data. • Cumulative mapping error correction for both loop and non-loop areas. • A novel indoor map format – lightweight point cloud map. [ABSTRACT FROM AUTHOR]