scholarly journals INDOOR LIDAR RELOCALIZATION BASED ON DEEP LEARNING USING A 3D MODEL

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 541-547
Author(s):  
H. Zhao ◽  
D. Acharya ◽  
M. Tomko ◽  
K. Khoshelham
Keyword(s):  
Indoor Environment ◽  
3D Model ◽  
Indoor Localization ◽  
High Accuracy ◽  
Lidar Data ◽  
Indoor Environments ◽  
Navigation Systems ◽  
Range Image ◽  
Range Images ◽  
Mapping Systems

Abstract. Indoor localization, navigation and mapping systems highly rely on the initial sensor pose information to achieve a high accuracy. Most existing indoor mapping and navigation systems cannot initialize the sensor poses automatically and consequently these systems cannot perform relocalization and recover from a pose estimation failure. For most indoor environments, a map or a 3D model is often available, and can provide useful information for relocalization. This paper presents a novel relocalization method for lidar sensors in indoor environments to estimate the initial lidar pose using a CNN pose regression network trained using a 3D model. A set of synthetic lidar frames are generated from the 3D model with known poses. Each lidar range image is a one-channel range image, used to train the CNN pose regression network from scratch to predict the initial sensor location and orientation. The CNN regression network trained by synthetic range images is used to estimate the poses of the lidar using real range images captured in the indoor environment. The results show that the proposed CNN regression network can learn from synthetic lidar data and estimate the pose of real lidar data with an accuracy of 1.9 m and 8.7 degrees.

scholarly journals Received signal strength–based localization for large space indoor environments

2017 ◽  
Vol 13 (1) ◽  
pp. 155014771668657 ◽  
Author(s):  
Xingwang Wang ◽  
Xiaohui Wei ◽  
Yuanyuan Liu ◽  
Shang Gao
Keyword(s):  
Indoor Environment ◽  
Indoor Localization ◽  
Sensor Data ◽  
Indoor Environments ◽  
Large Space ◽  
Research Attention ◽  
Novel Approach ◽  
Strength Based ◽  
Space Environments ◽  
Space Layout

WiFi-based indoor localization has attracted recent research attention. Large space layout is a special and more complex indoor environment. Most existing indoor localization methods lead to poor accuracy and many of them are not suitable for large space environments. In this article, we propose a novel approach for indoor localization and navigation. In our approach, the expensive training is avoided by utilizing the concept of pre-scheduled path and automatically mapping the WiFi fingerprints to it. For online tracing, we utilize historical sensor data to delineate users’ trajectory and calculate the similarity to all possible paths on the map, then the system chooses the most similar one as the result. The proposed work is evaluated and compared with previous methods. The results show that our approach improves accuracy by 80%.

scholarly journals TOPOLOGICAL 3D MODELING USING INDOOR MOBILE LIDAR DATA

2015 ◽  
Vol XL-4/W5 ◽  
pp. 13-18 ◽  
Author(s):  
M. Nakagawa ◽  
T. Yamamoto ◽  
S. Tanaka ◽  
M. Shiozaki ◽  
T. Ohhashi
Keyword(s):  
3D Modeling ◽  
Point Cloud ◽  
Indoor Environment ◽  
Lidar Data ◽  
Range Image ◽  
Mobile Mapping ◽  
Massive Point ◽  
Surface Estimation ◽  
Mapping System ◽  
Mobile Mapping System

We focus on a region-based point clustering to extract a polygon from a massive point cloud. In the region-based clustering, RANSAC is a suitable approach for estimating surfaces. However, local workspace selection is required to improve a performance in a surface estimation from a massive point cloud. Moreover, the conventional RANSAC is hard to determine whether a point lies inside or outside a surface. In this paper, we propose a method for panoramic rendering-based polygon extraction from indoor mobile LiDAR data. Our aim was to improve region-based point cloud clustering in modeling after point cloud registration. First, we propose a point cloud clustering methodology for polygon extraction on a panoramic range image generated with point-based rendering from a massive point cloud. Next, we describe an experiment that was conducted to verify our methodology with an indoor mobile mapping system in an indoor environment. This experiment was wall-surface extraction using a rendered point cloud from some viewpoints over a wide indoor area. Finally, we confirmed that our proposed methodology could achieve polygon extraction through point cloud clustering from a complex indoor environment.

scholarly journals Calibration-Free Single-Anchor Indoor Localization Using an ESPAR Antenna

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3431
Author(s):  
Mateusz Groth ◽  
Krzysztof Nyka ◽  
Lukasz Kulas
Keyword(s):  
Indoor Environment ◽  
Indoor Localization ◽  
Low Cost ◽  
Indoor Environments ◽  
Calibration Free ◽  
Architecture And Design ◽  
Positioning Algorithm ◽  
Parasitic Array ◽  
Better Than ◽  
Localization Systems

In this paper, we present a novel, low-cost approach to indoor localization that is capable of performing localization processes in real indoor environments and does not require calibration or recalibration procedures. To this end, we propose a single-anchor architecture and design based on an electronically steerable parasitic array radiator (ESPAR) antenna and Nordic Semiconductor nRF52840 utilizing Bluetooth Low Energy (BLE) protocol. The proposed algorithm relies on received signal strength (RSS) values measured by the receiver equipped with the ESPAR antenna for every considered antenna radiation pattern. The calibration-free concept is achieved by using inexpensive BLE nodes installed in known positions on the walls of the test room and acting as reference nodes for the positioning algorithm. Measurements performed in the indoor environment show that the proposed approach can successfully provide positioning results better than those previously reported for single-anchor ESPAR antenna localization systems employing the classical fingerprinting method and relying on time-consuming calibration procedures.

scholarly journals Tools for modelling distance estimation based on RSSI

2020 ◽  
Author(s):  
D. Dobrilovic ◽  
Z. Stojanov ◽  
J. Stojanov ◽  
M. Malic
Keyword(s):  
Indoor Localization ◽  
Distance Estimation ◽  
Signal Strength ◽  
Software Tools ◽  
Sensor Nodes ◽  
Indoor Environments ◽  
Navigation Systems ◽  
Microsoft Access ◽  
Main Challenge ◽  
Satellite Navigation Systems

The systems for localization of resources in indoor environments based on Received Signal Strength Indicator (RSSI) are widely used today. Since satellite navigation systems, such as GPS or GLONASS, have certain difficulties in the indoor environments, the signals of deployed wireless devices, such as sensor nodes, access points etc, are used for localization instead. Those systems are known as Indoor Positioning System (IPS). Those systems are used for resource tracking and positioning in places such as airports, railway stations, shopping malls, warehouses, production facilities, construction sites, and healthcare institutions. The Bluetooth Low Energy is one of the wireless technologies that can be used with great efficiency for indoor localization. It offers easy and economic implementation on mobile devices such as smart phones and tablets. There are many techniques used for determination of position. In a number of methods, such as ROCRSSI or MinMax, the distance from the wireless nodes is used for calculating the location. In those systems the main challenge is to accurately estimate distance from the device based on signal strength. In this paper, usability of various software tools for modelling the distance estimation based on RSSI is discussed. Those software tools are Microsoft Access, R Studio, Octave, and Python.

scholarly journals Panoramic rendering-based polygon extraction from indoor mobile LiDAR data

2014 ◽  
Vol XL-4 ◽  
pp. 181-186 ◽  
Author(s):  
M. Nakagawa ◽  
K. Kataoka ◽  
T. Yamamoto ◽  
M. Shiozaki ◽  
T. Ohhashi
Keyword(s):  
Point Cloud ◽  
Indoor Environment ◽  
Lidar Data ◽  
Range Image ◽  
Wall Surface ◽  
Mobile Mapping ◽  
Point Cloud Registration ◽  
Massive Point ◽  
Mapping System ◽  
Mobile Mapping System

In this paper, we propose a method for panoramic point-cloud rendering-based polygon extraction from indoor mobile LiDAR data. Our aim was to improve region-based point-cloud clustering in modeling after point-cloud registration. First, we propose a pointcloud clustering methodology for polygon extraction on a panoramic range image generated with point-based rendering from a massive point cloud. Next, we describe an experiment that was conducted to verify our methodology with an indoor mobile mapping system in an indoor environment. This experiment was wall-surface extraction using a rendered point-cloud from 64 viewpoints over a wide indoor area. Finally, we confirmed that our proposed methodology could achieve polygon extraction through point-cloud clustering from a complex indoor environment.

scholarly journals RETHINKING INDOOR LOCALIZATION SOLUTIONS TOWARDS THE FUTURE OF MOBILE LOCATION-BASED SERVICES

2017 ◽  
Vol IV-4/W4 ◽  
pp. 235-247 ◽  
Author(s):  
C. Guney
Keyword(s):  
Indoor Localization ◽  
Smart Cities ◽  
Indoor Navigation ◽  
Location Based Services ◽  
Outdoor Environment ◽  
Indoor Environments ◽  
Navigation Systems ◽  
Positioning Systems ◽  
Indoor Location ◽  
High Level

Satellite navigation systems with GNSS-enabled devices, such as smartphones, car navigation systems, have changed the way users travel in outdoor environment. GNSS is generally not well suited for indoor location and navigation because of two reasons: First, GNSS does not provide a high level of accuracy although indoor applications need higher accuracies. Secondly, poor coverage of satellite signals for indoor environments decreases its accuracy. So rather than using GNSS satellites within closed environments, existing indoor navigation solutions rely heavily on installed sensor networks. There is a high demand for accurate positioning in wireless networks in GNSS-denied environments. However, current wireless indoor positioning systems cannot satisfy the challenging needs of indoor location-aware applications. Nevertheless, access to a user’s location indoors is increasingly important in the development of context-aware applications that increases business efficiency. In this study, how can the current wireless location sensing systems be tailored and integrated for specific applications, like smart cities/grids/buildings/cars and IoT applications, in GNSS-deprived areas.

scholarly journals Dynamic Indoor Localization Using Maximum Likelihood Particle Filtering

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1090
Author(s):  
Wenxu Wang ◽  
Damián Marelli ◽  
Minyue Fu
Keyword(s):  
Maximum Likelihood ◽  
Particle Filtering ◽  
Indoor Localization ◽  
Real Data ◽  
High Accuracy ◽  
Classical Particle ◽  
Popular Approach ◽  
Filtering Method ◽  
Essential Idea ◽  
Number Of Particles

A popular approach for solving the indoor dynamic localization problem based on WiFi measurements consists of using particle filtering. However, a drawback of this approach is that a very large number of particles are needed to achieve accurate results in real environments. The reason for this drawback is that, in this particular application, classical particle filtering wastes many unnecessary particles. To remedy this, we propose a novel particle filtering method which we call maximum likelihood particle filter (MLPF). The essential idea consists of combining the particle prediction and update steps into a single one in which all particles are efficiently used. This drastically reduces the number of particles, leading to numerically feasible algorithms with high accuracy. We provide experimental results, using real data, confirming our claim.

scholarly journals RTLIO: Real-Time LiDAR-Inertial Odometry and Mapping for UAVs

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3955
Author(s):  
Jung-Cheng Yang ◽  
Chun-Jung Lin ◽  
Bing-Yuan You ◽  
Yin-Long Yan ◽  
Teng-Hu Cheng
Keyword(s):  
Feedback Control ◽  
Real Time ◽  
High Frequency ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Sampling Rate ◽  
Outdoor Environment ◽  
Position Accuracy ◽  
Graph Optimization ◽  
Pose Graph Optimization

Most UAVs rely on GPS for localization in an outdoor environment. However, in GPS-denied environment, other sources of localization are required for UAVs to conduct feedback control and navigation. LiDAR has been used for indoor localization, but the sampling rate is usually too low for feedback control of UAVs. To compensate this drawback, IMU sensors are usually fused to generate high-frequency odometry, with only few extra computation resources. To achieve this goal, a real-time LiDAR inertial odometer system (RTLIO) is developed in this work to generate high-precision and high-frequency odometry for the feedback control of UAVs in an indoor environment, and this is achieved by solving cost functions that consist of the LiDAR and IMU residuals. Compared to the traditional LIO approach, the initialization process of the developed RTLIO can be achieved, even when the device is stationary. To further reduce the accumulated pose errors, loop closure and pose-graph optimization are also developed in RTLIO. To demonstrate the efficacy of the developed RTLIO, experiments with long-range trajectory are conducted, and the results indicate that the RTLIO can outperform LIO with a smaller drift. Experiments with odometry benchmark dataset (i.e., KITTI) are also conducted to compare the performance with other methods, and the results show that the RTLIO can outperform ALOAM and LOAM in terms of exhibiting a smaller time delay and greater position accuracy.

scholarly journals Performance Evaluation of Particulate Matter and Indoor Microclimate Monitors in University Classrooms under COVID-19 Restrictions

2021 ◽  
Vol 18 (14) ◽  
pp. 7363
Author(s):  
Laurentiu Predescu ◽  
Daniel Dunea
Keyword(s):  
Performance Evaluation ◽  
Particulate Matter ◽  
Air Quality ◽  
Indoor Environment ◽  
Indoor Environments ◽  
University Campus ◽  
Particle Counter ◽  
Thermal Environments ◽  
University Classrooms ◽  
Academic Personnel

Optical monitors have proven their versatility into the studies of air quality in the workplace and indoor environments. The current study aimed to perform a screening of the indoor environment regarding the presence of various fractions of particulate matter (PM) and the specific thermal microclimate in a classroom occupied with students in March 2019 (before COVID-19 pandemic) and in March 2021 (during pandemic) at Valahia University Campus, Targoviste, Romania. The objectives were to assess the potential exposure of students and academic personnel to PM and to observe the performances of various sensors and monitors (particle counter, PM monitors, and indoor microclimate sensors). PM1 ranged between 29 and 41 μg m−3 and PM10 ranged between 30 and 42 μg m−3. It was observed that the particles belonged mostly to fine and submicrometric fractions in acceptable thermal environments according to the PPD and PMV indices. The particle counter recorded preponderantly 0.3, 0.5, and 1.0 micron categories. The average acute dose rate was estimated as 6.58 × 10−4 mg/kg-day (CV = 14.3%) for the 20–40 years range. Wearing masks may influence the indoor microclimate and PM levels but additional experiments should be performed at a finer scale.

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