Objects In Indoor Environments Research Articles

Path Clustering for Radio Propagation Prediction With Moving Object in Indoor Environment

Path Clustering for Radio Propagation Prediction With Moving Object in Indoor Environment

Machine Learning-based Indoor Positioning Systems Using Multi-Channel Information

The received signal strength indicator (RSSI) is a metric of the power measured by a sensor in a receiver. Many indoor positioning technologies use RSSI to locate objects in indoor environments. Their positioning accuracy is significantly affected by reflection and absorption from walls, and by non-stationary objects such as doors and people. Therefore, it is necessary to increase transceivers in the environment to reduce positioning errors. This paper proposes an indoor positioning technology that uses the machine learning algorithm of channel state information (CSI) combined with fingerprinting. The experimental results showed that the proposed method outperformed traditional RSSI-based localization systems in terms of average positioning accuracy up to 6.13% and 54.79% for random forest (RF) and back propagation neural networks (BPNN), respectively.

Development of real-time monitoring BLE-LoRa positioning system based on RSSI for non-line-of-sight condition

Indoor positioning has become popular in this decade and is used to locate users or objects in indoor environments. This is because global positioning system (GPS) is not efficient for indoor use due to the multipath fading effect. This research is about development bluetooth low energy (BLE) indoor positioning system with the aid of long range (LoRa) network and guideline on selection of the BLE beacons. Next, positioning systems are developed consisting of BLE beacons, a transceiver of hybrid BLE-LoRa module, a LoRa receiver and Raspberry Pi as real-time monitoring. The received signal strength indicator (RSSI) and BLE Mac address from BLE beacons received via LoRa network are analyzed using the positioning algorithm designed in MATLAB. The positioning algorithm incorporates distance estimation, filter implementation and trilateration technique. The estimated location is analyzed with the root mean square error (RMSE) and cumulative distribution function (CDF). According to the results, implementing the filter reduces the positioning accuracy error, achieving 90% accuracy of positioning error less than 1.20 meters for the whole testbed. Finally, the algorithm is embedded into Raspberry Pi to view the location via desktop.

A Dual-Encoder-Condensed Convolution Method for High-Precision Indoor Positioning

We study the problem of indoor positioning, which is a fundamental service in managing and analyzing objects in indoor environments. Unpredictable signal interference sources increase the degeneration of the accuracy and robustness of existing solutions. Deep learning approaches have recently been widely studied to overcome these challenges and attain better performance. In this paper, we aim to develop efficient algorithms, such as the dual-encoder-condensed convolution (DECC) method, which can achieve high-precision positioning for indoor services. In particular, firstly, we develop a convolutional module to add the original channel state information to the location information. Secondly, to explore channel differences between different antennas, we adopt a dual-encoder stacking mechanism for parallel calculation. Thirdly, we develop two different convolution kernels to speed up convergence. Performance studies on the indoor scenario and the urban canyon scenario datasets demonstrate the efficiency and effectiveness of our new approach.

Design of laser scanning binocular stereo vision imaging system and target measurement

Binocular stereo vision is an important scheme of road environment perception for intelligent driving, because it can provide image information and spatial three-dimensional position information. However, binocular stereo matching is often disturbed by the environment, and noise, shadow, light transformation, and so on, will all cause mismatching, therefore it is impossible to carry out accurate 3D reconstruction. In this paper, an active imaging perception and target measurement method combining laser scanning and binocular vision is proposed. The binocular vision structure is used to simulate the human eye's observation of the environment in front of the vehicle. At the same time, through the laser lines scanned to the target, the strong characteristics of binocular matching are provided to enhance the anti-interference of binocular matching, and calculate the three-dimensional point cloud data of the front object according to the binocular stereo vision theory. Firstly, this paper studies the influence of the parameters changing of the system on the accuracy of object depth value in indoor environment, and then selects the most suitable system parameters to carry out three-dimensional reconstruction experiment on the actual target object in indoor environment. Finally, the scene experiment in outdoor environment is carried out. In the outdoor environment, the red spectrum laser equipped in the system can still effectively reconstruct the target surface radian, but there are some limitations due to the limitation of outdoor light intensity. This paper preliminarily summarizes the current structural design and experiment, and further discusses the follow-up research and improvement ideas. Research and experiments show that the laser scanning binocular stereo vision imaging system proposed in this paper has the function of forward target ranging, especially under some bad imaging environment, this method can also effectively match the binocular images, and present the three-dimensional information of the internal details of the target. Under the indoor experimental environment, the minimum relative error is 1.42 % for the target with a distance of about 1.5 m, and in the evening outdoor environment, the minimum the minimum relative error is 0.87 % for the target with a distance of about 3 m. Due to the advantages of laser line intensity feature matching, it can accelerate the speed of binocular matching, and has potential application prospects in autonomous system.

Improving the Accuracy of Estimates of Indoor Distance Moved Using Deep Learning-Based Movement Status Recognition.

As a result of the development of wireless indoor positioning techniques such as WiFi, Bluetooth, and Ultra-wideband (UWB), the positioning traces of moving people or objects in indoor environments can be tracked and recorded, and the distances moved can be estimated from these data traces. These estimates are very useful in many applications such as workload statistics and optimized job allocation in the field of logistics. However, due to the uncertainties of the wireless signal and corresponding positioning errors, accurately estimating movement distance still faces challenges. To address this issue, this paper proposes a movement status recognition-based distance estimating method to improve the accuracy. We divide the positioning traces into segments and use an encoder–decoder deep learning-based model to determine the motion status of each segment. Then, the distances of these segments are calculated by different distance estimating methods based on their movement statuses. The experiments on the real positioning traces demonstrate the proposed method can precisely identify the movement status and significantly improve the distance estimating accuracy.

UWB-Based Indoor Localization: How to Optimally Design the Operating Setup?

Localizing objects in indoor environments with anchor–based systems poses several challenges to be faced. The main one is the design phase. What is the minimum number of anchors to be adopted? What is the optimal placement of the anchors in the operating domain? These are typical issues that needs to be solved in this stage. Such issue is generally addressed by several experimental tests, imposing high costs both in the required large amounts of time and in the use of devices. In addition, sometimes the experiments will not warrant to identify the optimal design solution. In this work, a black–box design tool is proposed, able to manage different environments, in terms of domain size, maximum number of available anchors, device metrological features, target performance. Such an approach, to be effective, needs a preliminary validation versus experimental results to make the future performance predictions reliable and avoid unexpected localization accuracy degradation. For this reason, the presented work adopts a side-by-side development, by evaluating results’ compatibility, in static and dynamic contexts, before addressing further and more complex analyses relying on the design tool only. Such comparison proves the goodness of the developed design tool and the outcomes allow an accurate localization system design by finding anchors’ placement solutions with the minimum computational burden.

Using the Web to Interactively Learn to Find Objects

In order for robots to intelligently perform tasks with humans, they must be able to access a broad set of background knowledge about the environments in which they operate. Unlike other approaches, which tend to manually define the knowledge of the robot, our approach enables robots to actively query the World Wide Web (WWW) to learn background knowledge about the physical environment. We show that our approach is able to search the Web to infer the probability that an object, such as a "coffee,'' can be found in a location, such as a "kitchen.'' Our approach, called ObjectEval, is able to dynamically instantiate a utility function using this probability, enabling robots to find arbitrary objects in indoor environments. Our experimental results show that the interactive version of ObjectEval visits 28% fewer locations than the version trained offline and 71% fewer locations than a baseline approach which uses no background knowledge.

Improved Indoor Positioning by Means of Occupancy Grid Maps Automatically Generated from OSM Indoor Data

In recent years, there is a growing interest in indoor positioning due to the increasing amount of applications that employ position data. Current approaches determining the location of objects in indoor environments are facing problems with the accuracy of the sensor data used for positioning. A solution to compensate inaccurate and unreliable sensor data is to include further information about the objects to be positioned and about the environment into the positioning algorithm. For this purpose, occupancy grid maps (OGMs) can be used to correct such noisy data by modelling the occupancy probability of objects being at a certain location in a specific environment. In that way, improbable sensor measurements can be corrected. Previous approaches, however, have focussed only on OGM generation for outdoor environments or require manual steps. There remains need for research examining the automatic generation of OGMs from detailed indoor map data. Therefore, our study proposes an algorithm for automated OGM generation using crowd-sourced OpenStreetMap indoor data. Subsequently, we propose an algorithm to improve positioning results by means of the generated OGM data. In our study, we used positioning data from an Ultra-wideband (UWB) system. Our experiments with nine different building map datasets showed that the proposed method provides reliable OGM outputs. Furthermore, taking one of these generated OGMs as an example, we demonstrated that integrating OGMs in the positioning algorithm increases the positioning accuracy. Consequently, the proposed algorithms now enable the integration of environmental information into positioning algorithms to finally increase the accuracy of indoor positioning applications.

Enhanced Fingerprinting Based Indoor Positioning Using Machine Learning

Due to the inability of the Global Positioning System (GPS) signals to penetrate through surfaces like roofs, walls, and other objects in indoor environments, numerous alternative methods for user positioning have been presented. Amongst those, the Wi-Fi fingerprinting method has gained considerable interest in Indoor Positioning Systems (IPS) as the need for line-of-sight measurements is minimal, and it achieves better efficiency in even complex indoor environments. Offline and online are the two phases of the fingerprinting method. Many researchers have highlighted the problems in the offline phase as it deals with huge datasets and validation of Fingerprints without pre-processing of data becomes a concern. Machine learning is used for the model training in the offline phase while the locations are estimated in the online phase. Many researchers have considered the concerns in the offline phase as it deals with huge datasets and validation of Fingerprints becomes an issue. Machine learning algorithms are a natural solution for winnowing through large datasets and determining the significant fragments of information for localization, creating precise models to predict an indoor location. Large training sets are a key for obtaining better results in machine learning problems. Therefore, an existing WLAN fingerprinting-based multi-story building location database has been used with 21049 samples including 19938 training and 1111 testing samples. The proposed model consists of mean and median filtering as pre-processing techniques applied to the database for enhancing the accuracy by mitigating the impact of environmental dispersion and investigated machine learning algorithms (kNN, WkNN, FSkNN, and SVM) for estimating the location. The proposed SVM with median filtering algorithm gives a reduced mean positioning error of 0.7959 m and an improved efficiency of 92.84% as compared to all variants of the proposed method for 108703 m2 area.

3D Semantic VSLAM of Indoor Environment Based on Mask Scoring RCNN

In view of existing Visual SLAM (VSLAM) algorithms when constructing semantic map of indoor environment, there are problems with low accuracy and low label classification accuracy when feature points are sparse. This paper proposed a 3D semantic VSLAM algorithm called BMASK-RCNN based on Mask Scoring RCNN. Firstly, feature points of images are extracted by Binary Robust Invariant Scalable Keypoints (BRISK) algorithm. Secondly, map points of reference key frame are projected to current frame for feature matching and pose estimation, and an inverse depth filter is used to estimate scene depth of created key frame to obtain camera pose changes. In order to achieve object detection and semantic segmentation for both static objects and dynamic objects in indoor environments and then construct dense 3D semantic map with VSLAM algorithm, a Mask Scoring RCNN is used to adjust its structure partially, where a TUM RGB-D SLAM dataset for transfer learning is employed. Semantic information of independent targets in scenes provides semantic information including categories, which not only provides high accuracy of localization but also realizes the probability update of semantic estimation by marking movable objects, thereby reducing the impact of moving objects on real-time mapping. Through simulation and actual experimental comparison with other three algorithms, results show the proposed algorithm has better robustness, and semantic information used in 3D semantic mapping can be accurately obtained.

QA-kNN: Indoor Localization Based on Quartile Analysis and the kNN Classifier for Wireless Networks.

Considering the variation of the received signal strength indicator (RSSI) in wireless networks, the objective of this study is to investigate and propose a method of indoor localization in order to improve the accuracy of localization that is compromised by RSSI variation. For this, quartile analysis is used for data pre-processing and the k-nearest neighbors (kNN) classifier is used for localization. In addition to the tests in a real environment, simulations were performed, varying many parameters related to the proposed method and the environment. In the real environment with reference points of 1.284 density per unit area (RPs/m2), the method presents zero-mean error in the localization in test points (TPs) coinciding with the RPs. In the simulated environment with a density of 0.327 RPs/m2, a mean error of 0.490 m for the localization of random TPs was achieved. These results are important contributions and allow us to conclude that the method is promising for locating objects in indoor environments.

An UWB Channel Impulse Response De-Noising Method for NLOS/LOS Classification Boosting

With the booming demand of indoor position information, Wireless signals (Wi-Fi, Bluetooth, Ultra-Wide-Band etc.) are investigated to construct Indoor Positioning System (IPS). Among these wireless signals, UWB (Ultra-Wide-Band) is recognized as the most promising technology to construct IPS with decimeter-level positioning accuracy. However, there are various objects in indoor environments, UWB signals might be reflected by these surrounding objects. These None-Line-Of-Sight (NLOS) signals will induce additional errors to the distance measurements between the anchor and agent. Therefore, NLOS/LOS (Line-Of-Sight) signals classification should be carried out for identifying the NLOS reception. In UWB based IPS, the distance information is extracted through the Channel Impulse Response (CIR) waveforms. NLOS reception will lead to different CIR waveforms compared with that from the LOS signal, this difference could be characterized for NLOS/LOS signals classification. However, the hardware circuits, signal transmitting path and the NLOS reception will bring about noises to the CIR waveforms, which might influence the signal type identification. Motivated by this problem, in this letter, a reversible transformation method was proposed for de-nosing the CIR data, and a Conventional Neural Network (CNN) was employed to identify the NLOS signal. Raw and de-noised CIR dataset were both input to the CNN for assessing the effect of the de-noising method. With dataset collected from seven different sites, the results showed that the reversible transformation method was able to increase the Signal Noise Ratio (SNR) values of these CIR datasets. After the de-noising, the CNN performed higher classification accuracy with less convolutional layers. Moreover, the de-noising operation brought about higher NLOS signal identification accuracy increase than that of LOS signals.

GRNet: Geometric relation network for 3D object detection from point clouds

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.

Indoor Localization for Passive Moving Objects Based on a Redundant SIMO Radar Sensor

In the past decade, many efforts have been devoted to indoor localization solutions. While significant progresses have been achieved, short-range localization of passive moving objects in indoor environments remains a technical challenge, especially when available radio spectrum is limited. In this paper, we resolve this challenge by proposing a single-frequency continuous-wave Doppler radar sensor implemented with a redundant single-input multiple-output architecture. Since only Doppler phase shift information is used in the proposed tracking-localization solution, the localization is naturally immune to stationary clutters such as walls and furniture, making this indoor localization only responsive to moving objects. Only using a single frequency is required, such an indoor localization can be implemented with the highest spectrum and energy efficiency. The obtained results imply the potential for the detection of human activities and vital signs in indoor environments with strong stationary scatters.

Evolution of Indoor Positioning Technologies: A Survey

Indoor positioning systems (IPS) use sensors and communication technologies to locate objects in indoor environments. IPS are attracting scientific and enterprise interest because there is a big market opportunity for applying these technologies. There are many previous surveys on indoor positioning systems; however, most of them lack a solid classification scheme that would structurally map a wide field such as IPS, or omit several key technologies or have a limited perspective; finally, surveys rapidly become obsolete in an area as dynamic as IPS. The goal of this paper is to provide a technological perspective of indoor positioning systems, comprising a wide range of technologies and approaches. Further, we classify the existing approaches in a structure in order to guide the review and discussion of the different approaches. Finally, we present a comparison of indoor positioning approaches and present the evolution and trends that we foresee.

Object Detection Applied to Indoor Environments for Mobile Robot Navigation.

To move around the environment, human beings depend on sight more than their other senses, because it provides information about the size, shape, color and position of an object. The increasing interest in building autonomous mobile systems makes the detection and recognition of objects in indoor environments a very important and challenging task. In this work, a vision system to detect objects considering usual human environments, able to work on a real mobile robot, is developed. In the proposed system, the classification method used is Support Vector Machine (SVM) and as input to this system, RGB and depth images are used. Different segmentation techniques have been applied to each kind of object. Similarly, two alternatives to extract features of the objects are explored, based on geometric shape descriptors and bag of words. The experimental results have demonstrated the usefulness of the system for the detection and location of the objects in indoor environments. Furthermore, through the comparison of two proposed methods for extracting features, it has been determined which alternative offers better performance. The final results have been obtained taking into account the proposed problem and that the environment has not been changed, that is to say, the environment has not been altered to perform the tests.

Antipodal Linear Tapered Slot Antenna Based Radio Link Characterization in Narrow Hallway Environment at 60 GHz

The performance of wireless communication systems is predominantly dependent on propagation environment and respective radiating antennas. Due to the shorter wavelength at millimeter wave (MmW) frequencies, the propagation loss through the objects in indoor environments is typically very high. To improve the channel capacity and to reduce inter-user interference, a high gain directional antenna is desired at MmW frequencies. Traditional antennas used in MmW devices are not suitable for low-cost commercial devices due to their heavy and bulky configurations. This paper focuses on design and development of a very compact (44.61 × 9.93 × 0.381 mm) high gain antipodal linear tapered slot antenna (ALTSA) utilizing substrate integrated waveguide (SIW) technology at 60 GHz. Received signal strength (RSS), path loss, and capacity are studied for MmW indoor applications utilizing ALTSA with radio frequency (RF) measurement equipment in narrow hallway environment.

Analysis of Geo Positioning System Using RF & DA

Indoor positioning systems (IPSs) have been intended to provide position information of persons and devices. Higher user percentage of handheld devices such as tablets or mobile phones had led to the development of a number of indoor positioning systems. In this research a work on a real time portable RFID indoor positioning device such as on smartphone will be performed. The personal networks will be designed to meet the users’ needs and interconnect users’ devices equipped with different communications technologies in various places to form one network for better result. Radio frequency identification (RFID) with directional antenna has proved its potential for locating objects in indoor environment. Hence, the proposed device idea will be used to exploit various unknown locations in an indoor environment such as college campus; this interpretation will rely on Wireless LAN, Received Signal Strength values from Access Points (AP) in specific mentioned arenas; these APs will be monitored constantly by RFID with directional antenna (DA) and handheld devices. For obtaining the better results from existing devices, algorithms of Range Estimation are proposed, which can be used on various handheld devices for locating indoor objects.

MineLoc — Personnel Tracking System for Longwall Coal Mining Sites

The localization of people and objects in indoor environments is an ongoing research challenge with a diverse set of possible positioning sensors. For the harsh environment of an underground coal mine, a system relying on received signal strength (RSS) measurements of common radio modules has been proven to deliver accurate and reliable positioning results. However, in tunnel environments of a longwall mining facility, conventional indoor radio propagation models are not valid due to waveguide effects. Additional investigations regarding the RSS-based ranging and the localization algorithm will be proposed. Ray tracing is used to find an appropriate propagation model and to define suitable range sections for different radio frequency bands. Simulation and experiment ranging results are compared for 2.4 GHz and sub- GHz radios. An enhanced weighted centroid localization (WCL) approach is essential, taking into account the peculiarities of the longwall mining environment.