Multi-floor Environments Research Articles

NV-LIOM: LiDAR-Inertial Odometry and Mapping Using Normal Vectors Towards Robust SLAM in Multifloor Environments

NV-LIOM: LiDAR-Inertial Odometry and Mapping Using Normal Vectors Towards Robust SLAM in Multifloor Environments

Enhancing Indoor User Localization: An Adaptive Bayesian Approach for Multi-Floor Environments

Enhancing Indoor User Localization: An Adaptive Bayesian Approach for Multi-Floor Environments

IPAL: Infinite Planes as Lines for Consistent Mapping in Indoor Multifloor Environments

IPAL: Infinite Planes as Lines for Consistent Mapping in Indoor Multifloor Environments

3D Absolute pose estimation of a staircase cleaning robot utilizing staircase geometry and minimal low-cost sensors

Thanks to its shape-shifting abilities, the sTetris robot can climb stairs while performing the cleaning task. The sTetris robot’s overall system operation depends on localization and positioning data, which are essential for its goal of autonomously navigating multi-floor environments. The mobile robots designed to work indoors generally rely on external systems for localization information. Regretfully, this frequently requires additional hardware fixing or changes to the indoor working environment in order to achieve accurate three-dimensional (3D) position and orientation (pose) for successful operation of the mobile platform. Nonetheless, the robot can be localized on the staircase by utilizing the information of the staircase’s geometry measurements, which are known ahead of time. This article demonstrates how the known geometry of staircases and measurements from minimal number of sensors can be used to accomplish 3D pose of the robot. Experiments carried out on a real robot in an authentic indoors setting successfully demonstrate the effectiveness of the suggested approach.

Staircase Recognition and Localization Using Convolutional Neural Network (CNN) for Cleaning Robot Application

Floor-cleaning robots are primarily designed to clean on a single floor, while multi-floor environments are usually not considered target applications. However, it is more efficient to have an autonomous floor-cleaning robot that can climb stairs and reach the next floors in a multi-floor building. To operate in such environments, the ability of a mobile robot to autonomously traverse staircases is very important. For this operation, staircase detection and localization are essential components for planning the traversal route on staircases. This article describes a deep learning approach using a convolutional neural network (CNN)-based robot operation system (ROS) framework for staircase detection, localization, and maneuvering of the robot to the detected stair. We present a real-time object detection framework to detect staircases in incoming images. We also localize these staircases using a contour detection algorithm to detect the target point: a point close to the center of the first step, and an angle of approach to the target point with respect to the current location of the robot. Experiments are performed with data from images captured on different types of staircases at different viewpoints/angles. The experimental results show that the presented approach can achieve an accuracy of 95% and a recall of 86.81%. A total runtime of 155 ms is taken to identify the presence of a staircase and the detection of the first step in the working environment, as well as being able to locate the target point with an accuracy of ±2 cm, ±1 degree.

A Federated Learning Framework for Fingerprinting-Based Indoor Localization in Multibuilding and Multifloor Environments

The participatory nature of federated learning (FL) makes it attractive for fingerprinting-based indoor localization in multibuilding and multifloor environments. A group of sensing clients can collaboratively leverage their private, local fingerprint data to help their edge server update a location prediction model. However, it is challenging to jointly handle the two involved issues, i.e., building-floor classification (BFC) and latitude–longitude regression (LLR), in a wide 3-D space through enabling FL on decentralized yet heterogeneous data and over an imperfect wireless network. In this article, we confront these challenges and propose an FL framework, FedLoc3D, for both BFC and LLR. Specifically, the former issue is addressed by an FedDSC-BFC approach, which generates a multilabel classification model based on a convolutional neural network with depthwise separable convolutions. The latter issue is addressed by an FedADA-LLR approach, which develops a multitarget regression model based on a deep neural network with autoencoder and data augmentation. Extensive experiments on a real-world data set of WiFi fingerprints are carried out, and our approaches with enhanced capabilities of feature extraction, generalization, and convergence are validated to improve both localization accuracy and learning efficiency under data heterogeneity and network instability.

Secure and Reliable Indoor Localization Based on Multitask Collaborative Learning for Large-Scale Buildings

Accurate and reliable indoor location estimate is crucial for many Internet-of-Things (IoT) applications in the era of smart buildings. However, the positioning accuracy and security of the existing positioning works cannot meet the demands in the large-scale smart buildings scenarios covering multiple multifloor buildings. Therefore, in this article, we focus on the reliable and accurate localization under multibuilding and multifloor environments. We propose two novel designs, including a two-step reliable feature selector and a multitask collaborative positioning model. First, we design a two-step reliable feature selector based on an access point (AP) confidence model and manifold learning, to help select the most representative and reliable fingerprint features. Second, we propose a multitask cooperative positioning model, which consists of a multiscale feature fusion module to adaptively fuse multiscale features and a multitask joint learning module to effectively constrain the cumulative error of multiscale position. Finally, based on the above two, we propose a reliable multibuilding and multifloor localization method (RMBMFL), which can achieve accurate and reliable location estimates with low computational complexity in a smart building complex. We did real-world experiments in a 20 000 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${m^{2}}$ </tex-math></inline-formula> site that covers three multistory buildings to evaluate the performance of the proposed RMBMFL. The experimental results show that RMBMFL achieves a building identification accuracy and a floor identification accuracy of 99%, and a room-level indoor localization with an average positioning error within 2 m, and outperforms state-of-the-art solutions.

Intelligent Reflecting Surface-Based Non-LOS Human Activity Recognition for Next-Generation 6G-Enabled Healthcare System.

Human activity monitoring is a fascinating area of research to support autonomous living in the aged and disabled community. Cameras, sensors, wearables, and non-contact microwave sensing have all been suggested in the past as methods for identifying distinct human activities. Microwave sensing is an approach that has lately attracted much interest since it has the potential to address privacy problems caused by cameras and discomfort caused by wearables, especially in the healthcare domain. A fundamental drawback of the current microwave sensing methods such as radar is non-line-of-sight and multi-floor environments. They need precise and regulated conditions to detect activity with high precision. In this paper, we have utilised the publicly available online database based on the intelligent reflecting surface (IRS) system developed at the Communications, Sensing and Imaging group at the University of Glasgow, UK (references 39 and 40). The IRS system works better in the multi-floor and non-line-of-sight environments. This work for the first time uses algorithms such as support vector machine Bagging and Decision Tree on the publicly available IRS data and achieves better accuracy when a subset of the available data is considered along specific human activities. Additionally, the work also considers the processing time taken by the classier in training stage when exposed to the IRS data which was not previously explored.

XGBLoc: XGBoost-Based Indoor Localization in Multi-Building Multi-Floor Environments.

Location-based indoor applications with high quality of services require a reliable, accurate, and low-cost position prediction for target device(s). The widespread availability of WiFi received signal strength indicator (RSSI) makes it a suitable candidate for indoor localization. However, traditional WiFi RSSI fingerprinting schemes perform poorly due to dynamic indoor mobile channel conditions including multipath fading, non-line-of-sight path loss, and so forth. Recently, machine learning (ML) or deep learning (DL)-based fingerprinting schemes are often used as an alternative, overcoming such issues. This paper presents an extreme gradient boosting-based ML indoor localization scheme, simply termed as XGBLoc, that accurately classifies (or detects) the positions of mobile devices in multi-floor multi-building indoor environments. XGBLoc not only effectively reduces the RSSI dataset dimensionality but trains itself using structured synthetic labels (also termed as relational labels), rather than conventional independent labels, that classify such complex and hierarchical indoor environments well. We numerically evaluate the proposed scheme on the publicly available datasets and prove its superiority over existing ML or DL-based schemes in terms of classification and regression performance.

Extreme Learning Machine for Accurate Indoor Localization Using RSSI Fingerprints in Multifloor Environments

A new extreme learning machine (ELM) localization technique that uses received signal strength indicator fingerprints only is proposed for multifloor environments. This structured scheme forms multiple individual ELMs for the floors as well as for the geographically formed data clusters of each floor. Multifloor environments often have huge amount of training and online measurement data. To maximize efficiency, we develop a data preprocessing algorithm, aiming to: 1) efficiently extract out only the essential information from the vast amount of data sets and reduce the data dimension and 2) transform the floor-level data sets and positioning data sets of each floor into a proper structure that is suitable for the proposed ensemble ELM technique. The proposed solution is unique in that its offline phase exploits multiple individual ELMs for all floors to generate a set of floor-level classification functions with the preprocessed training data sets, and for each floor, it exploits multiple ELMs for the data clusters to generate a set of position regression functions. The online phase executes a coarse localization step to estimate the floor by using the floor-level classification functions and a refined step to estimate the position on the floor by using the position regression functions. The proposed algorithm and several existing algorithms are implemented to perform localization using the same measured datasets in a multistory building. For both floor estimation and localization on the floor, it outperforms existing schemes. For most cases, the performance gap is substantial.

Personal Guides: Heterogeneous Robots Sharing Personal Tours in Multi-Floor Environments.

GidaBot is an application designed to setup and run a heterogeneous team of robots to act as tour guides in multi-floor buildings. Although the tours can go through several floors, the robots can only service a single floor, and thus, a guiding task may require collaboration among several robots. The designed system makes use of a robust inter-robot communication strategy to share goals and paths during the guiding tasks. Such tours work as personal services carried out by one or more robots. In this paper, a face re-identification/verification module based on state-of-the-art techniques is developed, evaluated offline, and integrated into GidaBot’s real daily activities, to avoid new visitors interfering with those attended. It is a complex problem because, as users are casual visitors, no long-term information is stored, and consequently, faces are unknown in the training step. Initially, re-identification and verification are evaluated offline considering different face detectors and computing distances in a face embedding representation. To fulfil the goal online, several face detectors are fused in parallel to avoid face alignment bias produced by face detectors under certain circumstances, and the decision is made based on a minimum distance criterion. This fused approach outperforms any individual method and highly improves the real system’s reliability, as the tests carried out using real robots at the Faculty of Informatics in San Sebastian show.

GaitMesh: Controller-Aware Navigation Meshes for Long-Range Legged Locomotion Planning in Multi-Layered Environments

Long-range locomotion planning is an important problem for the deployment of legged robots to real scenarios. Current methods used for legged locomotion planning often do not exploit the flexibility of legged robots, and do not scale well with environment size. In this letter we propose the use of navigation meshes for deployment in large-scale multi-floor sites. We leverage this representation to improve long-term locomotion plans in terms of success rates, path costs and reasoning about which gait-controller to use when. We show that NavMeshes have higher planning success rates than sampling-based planners, but are 400x faster to construct and at least 100x faster to plan with. The performance gap further increases when considering multi-floor environments. We present both a procedure for building controller-aware NavMeshes and a full navigation system that adapts to changes to the environment. We demonstrate the capabilities of the system in simulation experiments and in field trials at a real-world oil rig facility.

GidaBot: a system of heterogeneous robots collaborating as guides in multi-floor environments

GidaBot is an application designed to set up and run a heterogeneous team of robots to act as tour guides in multi-floor buildings. Although the tours can go through several floors, robots are not allowed to use the lift, and thus, each guiding task requires collaboration among several robots, one per floor. The designed system makes use of a robust inter-robot communication strategy to share goals and paths during the guiding tasks. A user-friendly GUI helps untrained users or new visitors to easily choose target locations or define a list of locations to be visited sequentially. A prototype has been implemented using ROS, and the system robustness has been tested in a Gazebo-based simulated robot/environment and using real robots at the Faculty of Informatics in San Sebastian. The whole application is available together with a simulated world so that the system functioning can be checked further.

A Hybrid Model Based on Constraint OSELM, Adaptive Weighted SRC and KNN for Large-Scale Indoor Localization

In this paper, a novel hybrid model based on the constraint online sequential extreme learning machine (COSELM) classifier with adaptive weighted sparse representation classification (WSRC) and k nearest neighbor (KNN) is proposed for the WiFi-based indoor positioning system. It is referred to as A Fast-Accurate-Reliable Localization System (AFARLS). AFARLS exploits the speed advantage of COSELM to reduce the computational cost, and the accuracy advantage of WSRC to enhance the classification performance, by utilizing KNN as the adaptive sub-dictionary selection strategy. The understanding is that the original extreme learning machine (ELM) is less robust against noise, while sparse representation classification (SRC) and KNN suffer a high computational burden when using the over-complete dictionary. AFARLS unifies their complementary strengths to resolve each other’s limitation. In large-scale multi-building and multi-floor environments, AFARLS estimates a location that considers the building, floor, and position (longitude and latitude) in a hierarchical and sequential approach according to a discriminative criterion to the COSELM output. If the classifier result is unreliable, AFARLS uses KNN to achieve the best relevant sub-dictionary. The sub-dictionary is fed to WSRC to re-estimate the building and the floor, while the position is predicted by the ELM regressor. AFARLS has been verified on two publicly available datasets, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EU Zenodo</i> and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UJIIndoorLoc</i> . The experimental results demonstrate that AFARLS outperforms the state-of-the-art algorithms on the former dataset, and it provides near state-of-the-art performance on the latter dataset. When the size of the dataset increases remarkably, AFARLS shows that it can maintain its real-time high-accuracy performance.

Vertical Indexing for Moving Objects in Multifloor Environments

The indexing and tracking of objects moving in indoor spaces has increasingly become an important area of research, which presents a fundamentally different challenge. There are two main reasons for why indoor should be treated as cellular space. Firstly, an indoor space has entities, such as rooms and walls, that constrain the movement of the moving objects. Secondly, the relevant notion of locations of an object is cell based rather than an exact Euclidean coordinate. As a solution, in our earlier works, we proposed a cell-based indexing structure, called the C-tree, for indexing objects moving in indoor space. In this paper, we extend the C-tree to solve another interesting problem. It can be observed that many indoor spaces (such as shopping centers) contain wings/sections. For such a space, there are queries for which the wing/section location of an object, rather than the cellular location, is the relevant answer (e.g., “the object is in the east wing”). In this paper, we propose a new index structure, called the GMI-tree (“GMI” stands for “Graph-based Multidimensional Index”). The GMI-tree is based on two notions of distance, or equivalently, two notions of adjacency: one represents horizontal adjacency and the other represents vertical adjacency.

Adaptive Indoor Positioning Model Based on WLAN-Fingerprinting for Dynamic and Multi-Floor Environments.

The Global Positioning System demonstrates the significance of Location Based Services but it cannot be used indoors due to the lack of line of sight between satellites and receivers. Indoor Positioning Systems are needed to provide indoor Location Based Services. Wireless LAN fingerprints are one of the best choices for Indoor Positioning Systems because of their low cost, and high accuracy, however they have many drawbacks: creating radio maps is time consuming, the radio maps will become outdated with any environmental change, different mobile devices read the received signal strength (RSS) differently, and peoples’ presence in LOS between access points and mobile device affects the RSS. This research proposes a new Adaptive Indoor Positioning System model (called DIPS) based on: a dynamic radio map generator, RSS certainty technique and peoples’ presence effect integration for dynamic and multi-floor environments. Dynamic in our context refers to the effects of people and device heterogeneity. DIPS can achieve 98% and 92% positioning accuracy for floor and room positioning, and it achieves 1.2 m for point positioning error. RSS certainty enhanced the positioning accuracy for floor and room for different mobile devices by 11% and 9%. Then by considering the peoples’ presence effect, the error is reduced by 0.2 m. In comparison with other works, DIPS achieves better positioning without extra devices.

Comprehensive analysis of distance and similarity measures for Wi-Fi fingerprinting indoor positioning systems

Recent advances in indoor positioning systems led to a business interest in those applications and services where a precise localization is crucial. Wi-Fi fingerprinting based on machine learning and expert systems are commonly used in the literature. They compare a current fingerprint to a database of fingerprints, and then return the most similar one/ones according to: 1) a distance function, 2) a data representation method for received signal strength values, and 3) a thresholding strategy. However, most of the previous works simply use the Euclidean distance with the raw unprocessed data. There is not any previous work that studies which is the best distance function, which is the best way of representing the data and which is the effect of applying thresholding. In this paper, we present a comprehensive study using 51 distance metrics, 4 alternatives to represent the raw data (2 of them proposed by us), a thresholding based on the RSS values and the public UJIIndoorLoc database. The results shown in this paper demonstrate that researchers and developers should take into account the conclusions arisen in this work in order to improve the accuracy of their systems. The IPSs based on k-NN are improved by just selecting the appropriate configuration (mainly distance function and data representation). In the best case, 13-NN with Sørensen distance and the powed data representation, the error in determining the place (building and floor) has been reduced in more than a 50% and the positioning accuracy has been increased in 1.7 m with respect to the 1-NN with Euclidean distance and raw data commonly used in the literature. Moreover, our experiments also demonstrate that thresholding should not be applied in multi-building and multi-floor environments.

3D ActionSLAM: wearable person tracking in multi-floor environments

We present 3D ActionSLAM, a stand-alone wearable system that can track people in previously unknown multi-floor environments with sub-room accuracy. ActionSLAM stands for action-based simultaneous localization and mapping: It fuses dead reckoning data from a foot-mounted inertial measurement unit with the recognition of location-related actions to build and update a local landmark map. Simultaneously, this map compensates for position drift errors that accumulate in open-loop tracking by means of a particle filter. To evaluate the system performance, we analyzed 23 tracks with a total walked distance of 6,489 m in buildings with up to three floors. The algorithm robustly (93 % of runs converged) mapped the areas with a mean landmark positioning error of 0.59 m. As ActionSLAM is fully stand-alone and not dependent on external infrastructure, it is well suited for patient tracking in remote health care applications. The algorithm is computationally light-weight and runs in real-time on a Samsung Galaxy S4, enabling immediate location-aware feedback. Finally, we propose visualization techniques to facilitate the interpretation of tracking data acquired with 3D ActionSLAM.

Propagation Measurement and Modeling for Indoor Stairwells at 2.4 and 5.8 GHz

The propagation characteristics of indoor multi-floor environments have been studied extensively and empirical models for many scenarios are available. These studies usually do not concern about stair structures. In this paper we study radio propagation in four typical stairwells through measurement at two frequencies (2.4 GHz and 5.8 GHz). Values of path loss exponent n have been derived for vertical and horizontal polarizations. These n-values for stairwells are found to be higher than the n-values for multi-floor environments. We also propose a new path loss model based on the so-called “accumulative distance” the receiver has traveled, in addition to the conventional separation distance between transmitting and receiving antennas. The new path loss model has lower n values and, most importantly, smaller standard deviation and can thus be considered a better model fitting the measurement data. The results in this study can be useful for designers of small cell wireless communications system such as pico- and femto-cells.

Special Issue on Robotic Technologies Towards Industrial Applications

Robotics technologies have come to be widely developed for and applied to a variety of industrial areas, including the manufacturing, transportation, and public security fields. Broadly defined, the robotics technologies implemented in industry involve numerous flexible mechanisms, high-precision control, multi-sensor integration and fusion, high-performance computation, and intelligent software. The collection of articles featured in this special issue concerns several of the robotics technologies touched upon above, such as the mechanical design methodology and applications of manipulators in joint compliance research, the reliability design and implementation of robots utilized to feed glassmaking-materials in the glass industry, and the sensing techniques and navigation algorithm design in multi-floor structural environments, to mention just a few. These papers include too many subjects to all be mentioned here, and have presented exemplifications and applicable approaches to both conventional and cutting-edge industry. They have also greatly enhanced the research and development of robotic technologies. We would like to express our sincere appreciation to the authors, the reviewers, and the editors for their invaluable contributions in making this issue possible.