Large-scale Indoor Environments Research Articles

Forward and correctional OFDM-based visible light positioning

Visible light positioning (VLP) has attracted much attention in both academic and industrial areas due to the extensive deployment of light-emitting diodes (LEDs) as next-generation green lighting. Generally, the coverage of a single LED lamp is limited, so LED arrays are always utilized to achieve uniform illumination within the large-scale indoor environment. However, in such dense LED deployment scenario, the superposition of the light signals becomes an important challenge for accurate VLP. To solve this problem, we propose a forward and correctional orthogonal frequency division multiplexing (OFDM)-based VLP (FCO-VLP) scheme with low complexity in generating and processing of signals. In the first forward procedure of FCO-VLP, an initial position is obtained by the trilateration method based on OFDM-subcarriers. The positioning accuracy will be further improved in the second correctional procedure based on the database of reference points. As demonstrated in our experiments, our approach yields an improved average positioning error of 4.65 cm and an enhanced positioning accuracy by 24.2% compared with trilateration method.

A Map-Assisted WiFi AP Placement Algorithm Enabling Mobile Device’s Indoor Positioning

Location information and positioning technology are important to some Internet of Things (IoT) applications. The accuracy of indoor positioning using WiFi can be substantially enhanced by appropriate access point (AP) placement strategies, i.e., in a given indoor environment to deploy the WiFi APs at the locations where the mobile devices can work out their location more precisely. The plan of AP placement needs to be generated automatically by algorithms, especially for large-scale indoor environment. This paper presents an indoor map system that provides coordinate system and graphic representation. The detailed map information such as walls can be explicitly expressed and used to assist the AP placement algorithm. In this paper, AP placement is formulated into an optimization problem in which the sum of Euclidean distance of fingerprints among all the reference points (RPs) is maximized. The fingerprint at RP is predicted by an indoor radio propagation model which takes the attenuation of walls into consideration with the assistance of our indoor map. The optimization problem is solved by particle swarm optimization (PSO) and evaluated by k-nearest neighbors positioning algorithm in a real-world environment. The experimental results show that our map-assisted AP placement can provide higher positioning accuracy.

Reconstructing the World’s Museums

Virtual exploration tools for large indoor environments (e.g. museums) have so far been limited to either blueprint-style 2D maps that lack photo-realistic views of scenes, or ground-level image-to-image transitions, which are immersive but ill-suited for navigation. On the other hand, photorealistic aerial maps would be a useful navigational guide for large indoor environments, but it is impossible to directly acquire photographs covering a large indoor environment from aerial viewpoints. This paper presents a 3D reconstruction and visualization system for automatically producing clean and well-regularized texture-mapped 3D models for large indoor scenes, from ground-level photographs and 3D laser points. The key component is a new algorithm called “inverse constructive solid geometry (CSG)” for reconstructing a scene with a CSG representation consisting of volumetric primitives, which imposes powerful regularization constraints. We also propose several novel techniques to adjust the 3D model to make it suitable for rendering the 3D maps from aerial viewpoints. The visualization system enables users to easily browse a large-scale indoor environment from a bird’s-eye view, locate specific room interiors, fly into a place of interest, view immersive ground-level panorama views, and zoom out again, all with seamless 3D transitions. We demonstrate our system on various museums, including the Metropolitan Museum of Art in New York City—one of the largest art galleries in the world.

Two-dimensional laser-based environment exploration and recognition for service robots

Environment exploration and recognition play an important role for service robots to operate in indoor environments that have a large scale and dynamic changes. This paper presents a new geometric model of the environment that is constructed with line segments and ellipses by estimating geometric properties and extracting geometric discontinuities. Two new types of indoor environment representations, namely exploration direction point and topological opening point (TOP), are defined using this model for autonomous exploration and spatial scene segmentation. The environment marks at TOPs are extracted from 2D laser data and a novel environment recognition algorithm is developed to accomplish environment recognition tasks autonomously. The experimental results show that the proposed approach can effectively explore and recognize a large-scale indoor environment in real time.

3-D-Laser-Based Scene Measurement and Place Recognition for Mobile Robots in Dynamic Indoor Environments

Active environment perception and autonomous place recognition play a key role for mobile robots to operate within a cluttered indoor environment with dynamic changes. This paper presents a 3-D-laser-based scene measurement technique and a novel place recognition method to deal with the random disturbances caused by unexpected movements of people and other objects. The proposed approach can extract and match the Speeded-Up Robust Features (SURFs) from bearing-angle images generated by a self-built rotating 3-D laser scanner. It can cope with the irregular disturbance of moving objects and the problem of observing-location changes of the laser scanner. Both global metric information and local SURF features are extracted from 3-D laser point clouds and 2-D bearing-angle images, respectively. A large-scale indoor environment with over 1600 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 30 offices is selected as a testing site, and a mobile robot, i.e., SmartROB2, is deployed for conducting experiments. Experimental results show that the proposed 3-D-laser-based scene measurement technique and place recognition approach are effective and provide robust performance of place recognition in a dynamic indoor environment.

Real-Time 3-D Mapping for Indoor Environments Using RGB-D Cameras

For three-dimensional (3-D) mapping, so far, 3-D laser scanners and stereo camera systems are used widely due to their high measurement range and accuracy. For stereo camera systems, establishing corresponding point pairs between two images is one crucial step for reconstructing depth information. However, mapping approaches using laser scanners are still restricted by a serious constraint by accurate image registration and mapping. In recent years, time-of-flight (ToF) cameras have been used for mapping tasks in providing high frame rates while preserving a compact size, but lack in measurement precision and robustness. To address the current technological bottleneck, this article presents a 3-D mapping method which employs an RGB-D camera for 3-D data acquisition and then applies the RGB-D features alignment (RGBD-FA) for data registration. Experimental results show the feasibility and robustness of applying the proposed approach for real-time 3-D mapping for large-scale indoor environments.

A HYBRID SENSING APPROACH TO MOBILE ROBOT LOCALIZATION IN COMPLEX INDOOR ENVIRONMENTS

This paper presents a hybrid sensing system for mobile robot localization in large-scale indoor environments. The system operates in two sensing modes, either omni-directional vision or laser scanning, according to the environmental characteristics. For a structured corridor environment, the vision information is adopted to track the robot pose with a predefined hybrid metric-topological map. For a semi-structured office room, the laser scanning mode is chosen to generate a sequence of relative pose transformations based on a scan matching algorithm. Kalman filters are deployed to smooth multiple scan matching results. The proposed hybrid sensing system can perform localization tasks on-the-fly, with the features of efficient map modelling and computational simplicity. Experimental results are provided to demonstrate the performance and effectiveness of the proposed techniques.

Interactive 3-D indoor modeler for virtualizing service fields

This paper describes an interactive 3-D indoor modeler that effectively creates photo-realistic 3-D indoor models from multiple photographs. This modeler supports the creation of 3-D models from photographs by implementing interaction techniques that use geometric constraints estimated from photographs and visualization techniques that help to easily understand shapes of 3-D models. We evaluated the availability and usability by applying the modeler to model service fields where actual workers provide services and an experience-based exhibit. Our results confirmed that the modeler enables the creation of large-scale indoor environments such as hot-spring inns and event sites at a relatively modest cost. We also confirmed that school children could learn modeling operations and create 3-D models from a photograph for approximately 20 min because of the easy operations. In addition, we describe additional functions that increase the effectiveness of 3-D modeling based on knowledge from service-field modeling. We present applications for behavior analysis of service workers and for 3-D indoor navigation using augmented virtuality (AV)-based visualization realized by photo-realistic 3-D models.

Large Scale Indoor Localization System Based on Wireless Sensor Networks for Ubiquitous Computing

The task of formulating an efficient system for determining the location of an object, results in the creation of a wide number of applications and services. For this reason, most wireless sensor network applications assume the availability of sensor location information. In this paper, an indoor localization scheme, which is based on synchronized sensor nodes, is proposed. It is efficient in terms of power consumption and location update rate. Furthermore, it resolves the scalability problem usually found in most conventional indoor localization systems in large scale indoor environments. The performance of the proposed scheme is evaluated through experimental implementation and is compared with the Cricket system. The results demonstrate that the proposed scheme is a promising and feasible localization system for a large scale indoor environment.

Simultaneous Localization and Mapping in a Hybrid Robot and Camera Network System

To obtain accurate grid maps of large environments equipped with camera networks in real time, a two-level mapping method is presented in this paper. The local map level is composed of a set of local maps. Once a mobile robot enters a new camera visual field, a new local map corresponding to the camera is built by a Rao-Blackwellized particle filter (RBPF) method using the data from the robot’s laser sensor and odometry. During the local mapping process, a camera-calibration problem is also solved by using a marker attached to the robot which moves as a curve fashion in the camera visual field. The global level is an adjacency graph whose arcs are labeled with the relative transformations between local maps. Among these relative transformations, of particular important are the revisiting constraints. When the robot returns a previous local map, it can be detected and relocated by a calibrated camera corresponding to the local map. Then a revisiting constraint can be attained and added into global constraints. To have an accurate and consistent global map, a stochastic gradient descent (SGD) algorithm based on a simple state space is employed to optimize the existed graph after adding and updating each global constraint. Experimental results carried out with a real mobile robot in a large-scale indoor environment illustrate the advantages of our methods over two current state-of-the-art approaches.

Omnidirectional Vision-based Self-localization by Using Large-scale Metric-topological 3D Map

Towards large-scale indoor environment, a novel metric-topological 3D map is proposed for robot self- localization based on omnidirectional vision. The local metric map, in a hierarchical manner, defines geometrical elements according to their environmental feature levels. Then, the topological parts in the global map are used to connect the adjacent local maps. We design a nonlinear omnidirectional camera model to project the probabilistic map elements with uncertainty manipulation. Therefore, image features can be extracted in the vicinity of corresponding projected curves. For the self-localization task, a human-machine interaction system is developed using a hierarchical logic. It provides a fusion center which adopts feedback hierarchical fusion method to fuse local estimates generated from multi-observations. Finally, a series of experiments are conducted to prove the reliable and practical performance of our system.

Development of a Tracking Method for Augmented Reality Applied to NPP Maintenance Work and Its Experimental Evaluation

Nuclear power plants (NPPs) must be maintained periodically. Their maintenance efficiency must be improved and human error must be reduced simultaneously to improve NPPs' competitive capability in electricity markets. Although Augmented Reality (AR) offers great possibilities to support NPP maintenance work, some difficulties exist for application of AR to actual work support because current AR systems cannot be implemented in NPP environments without technical improvement. There are several kinds of problems such as recognition distance, tracking accuracy, and a complex working environment when applying AR to NPP field work support. Considerable extension of tracking distance and improvement of accuracy are particularly desired because NPPs are large-scale indoor environments. This study designed a linecode marker, a new type of paper-based marker, along with recognition and tracking algorithms for it to resolve these problems. In contrast to conventional paper-based markers, such as square markers and circle markers, the linecode marker is not merely easier to set up in complex industrial environments: it also enables the use of AR in industrial plants because of its considerable tracking-performance improvement. To evaluate tracking accuracy, the trackable distance, and the tracking speed of the proposed tracking method, an evaluation experiment was conducted in a large room. The experiment results show that the tracking distance is extended extremely over that of the traditional marker-based tracking method: tracking accuracy improved to 20 cm over a 10 m distance. The running speed of the tracking can be as fast as 15 frames per second using a laptop PC.

Fast and accurate SLAM with Rao–Blackwellized particle filters

Rao–Blackwellized particle filters have become a popular tool to solve the simultaneous localization and mapping problem. This technique applies a particle filter in which each particle carries an individual map of the environment. Accordingly, a key issue is to reduce the number of particles and/or to make use of compact map representations. This paper presents an approximative but highly efficient approach to mapping with Rao–Blackwellized particle filters. Moreover, it provides a compact map model. A key advantage is that the individual particles can share large parts of the model of the environment. Furthermore, they are able to reuse an already computed proposal distribution. Both techniques substantially speed up the overall filtering process and reduce the memory requirements. Experimental results obtained with mobile robots in large-scale indoor environments and based on published standard datasets illustrate the advantages of our methods over previous mapping approaches using Rao–Blackwellized particle filters.

Triangulation-based fusion of sonar data with application in robot pose tracking

In this paper a sensor fusion scheme, called triangulation-based fusion (TBF) of sonar data, is presented. This algorithm delivers stable natural point landmarks, which appear in practically all indoor environments, i.e., vertical edges like door posts, table legs, and so forth. The landmark precision is in most cases within centimeters. The TBF algorithm is implemented as a voting scheme, which groups sonar measurements that are likely to have hit the same object in the environment. The algorithm has low complexity and is sufficiently fast for most mobile robot applications. As a case study, we apply the TBF algorithm to robot pose tracking. The pose tracker is implemented as a classic extended Kalman filter, which use odometry readings for the prediction step and TBF data for measurement updates. The TBF data is matched to pre-recorded reference maps of landmarks in order to measure the robot pose. In corridors, complementary TBF data measurements from the walls are used to improve the orientation and position estimate. Experiments demonstrate that the pose tracker is robust enough for handling kilometer distances in a large scale indoor environment containing a sufficiently dense landmark set.

Learning metric-topological maps for indoor mobile robot navigation

Autonomous robots must be able to learn and maintain models of their environments. Research on mobile robot navigation has produced two major paradigms for mapping indoor environments: grid-based and topological. While grid-based methods produce accurate metric maps, their complexity often prohibits efficient planning and problem solving in large-scale indoor environments. Topological maps, on the other hand, can be used much more efficiently, yet accurate and consistent topological maps are often difficult to learn and maintain in large-scale environments, particularly if momentary sensor data is highly ambiguous. This paper describes an approach that integrates both paradigms: grid-based and topological. Grid-based maps are learned using artificial neural networks and naive Bayesian integration. Topological maps are generated on top of the grid-based maps, by partitioning the latter into coherent regions. By combining both paradigms, the approach presented here gains advantages from both worlds: accuracy/consistency and efficiency. The paper gives results for autonomous exploration, mapping and operation of a mobile robot in populated multi-room environments.