scholarly journals A Particle PHD Filter for Dynamic Grid Map Building towards Indoor Environment

2021 ◽  
Vol 11 (15) ◽  
pp. 6891
Author(s):  
Yanjie Liu ◽  
Changsen Zhao ◽  
Yanlong Wei
Keyword(s):  
Indoor Environment ◽  
Response Speed ◽  
The State ◽  
Outdoor Environment ◽  
Estimation Accuracy ◽  
Map Building ◽  
Motion Model ◽  
Probability Hypothesis Density ◽  
Grid Map ◽  
Dynamic Objects

The PHD (Probability Hypothesis Density) filter is a sub-optimal multi-target Bayesian filter based on a random finite set, which is widely used in the tracking and estimation of dynamic objects in outdoor environments. Compared with the outdoor environment, the indoor environment space and the shape of dynamic objects are relatively small, which puts forward higher requirements on the estimation accuracy and response speed of the filter. This paper proposes a method for fast and high-precision estimation of the dynamic objects’ velocity for mobile robots in an indoor environment. First, the indoor environment is represented as a dynamic grid map, and the state of dynamic objects is represented by its grid cells state as random finite sets. The estimation of dynamic objects’ speed information is realized by using the measurement-driven particle-based PHD filter. Second, we bound the dynamic grid map to the robot coordinate system and derived the update equation of the state of the particles with the movement of the robot. At the same time, in order to improve the perception accuracy and speed of the filter for dynamic targets, the CS (Current Statistical) motion model is added to the CV (Constant Velocity) motion model, and interactive resampling is performed to achieve the combination of the advantages of the two. Finally, in the Gazebo simulation environment based on ROS (Robot Operating System), the speed estimation and accuracy analysis of the square and cylindrical dynamic objects were carried out respectively when the robot was stationary and in motion. The results show that the proposed method has a great improvement in effect compared with the existing methods.

scholarly journals A novel motion-model-free UWB short-range positioning method

2019 ◽  
Vol 14 (4) ◽  
pp. 815-820
Author(s):  
Navid Ayoobi ◽  
Mohammad Ghavami ◽  
Amir Masoud Rabiei
Keyword(s):  
Indoor Environment ◽  
Channel Model ◽  
Real Data ◽  
Location Based Services ◽  
Outdoor Environment ◽  
Motion Model ◽  
Floor Plan ◽  
Positioning Systems ◽  
Model Free ◽  
Positioning Method

AbstractIn recent years, the number of location-based services is increasing and consequently, the researchers’ attentions are captivated in designing accurate real-time positioning systems. Despite having a good performance in outdoor environment, global positioning system is not capable of estimating an object’s position in an indoor environment precisely. In this paper, we present a novel tracking algorithm for indoor environment with a known floor plan. The object location is estimated by utilizing the information of the multipath components which are created by one physical and some virtual anchors. We will link this information to the floor plan by defining a channel model that has a combination of stochastic and deterministic traits. As we have used only one physical anchor in this paper, we would encounter several challenges such as lack of data association and existence of clutters amid real data. We dealt with these problems through random finite set methodology. Additionally, we will demonstrate that the proposed method is not restricted by the model of the motion and is capable to precisely track the trajectory. It will be shown that it provides a better accuracy, particularly in nonlinear trajectories, compared with two other relevant models which are adopting linear motion model.

scholarly journals ROBUST SPARSE MATCHING AND MOTION ESTIMATION USING GENETIC ALGORITHMS

2015 ◽  
Vol XL-3/W2 ◽  
pp. 197-204 ◽  
Author(s):  
M. Shahbazi ◽  
G. Sohn ◽  
J. Théau ◽  
P. Ménard
Keyword(s):  
Motion Estimation ◽  
Computational Efficiency ◽  
State Of The Art ◽  
Random Search ◽  
Synthetic Data ◽  
The State ◽  
Estimation Accuracy ◽  
Motion Model ◽  
Least Trimmed Squares ◽  
Orientation Parameters

In this paper, we propose a robust technique using genetic algorithm for detecting inliers and estimating accurate motion parameters from putative correspondences containing any percentage of outliers. The proposed technique aims to increase computational efficiency and modelling accuracy in comparison with the state-of-the-art via the following contributions: i) guided generation of initial populations for both avoiding degenerate solutions and increasing the rate of useful hypotheses, ii) replacing random search with evolutionary search, iii) possibility of evaluating the individuals of every population by parallel computation, iv) being performable on images with unknown internal orientation parameters, iv) estimating the motion model via detecting a minimum, however more than enough, set of inliers, v) ensuring the robustness of the motion model against outliers, degeneracy and poorperspective camera models, vi) making no assumptions about the probability distribution of inliers and/or outliers residuals from the estimated motion model, vii) detecting all the inliers by setting the threshold on their residuals adaptively with regard to the uncertainty of the estimated motion model and the position of the matches. The proposed method was evaluated both on synthetic data and real images. The results were compared with the most popular techniques from the state-of-the-art, including RANSAC, MSAC, MLESAC, Least Trimmed Squares and Least Median of Squares. Experimental results proved that the proposed approach perform better than others in terms of accuracy of motion estimation, accuracy of inlier detection and the computational efficiency.

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 Detection of Legionella species, the influence of precipitation on the amount of Legionella DNA, and bacterial microbiome in aerosols from outdoor sites near asphalt roads in Toyama Prefecture, Japan

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jun-ichi Kanatani ◽  
Masanori Watahiki ◽  
Keiko Kimata ◽  
Tomoko Kato ◽  
Kaoru Uchida ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Indoor Environment ◽  
Geometric Mean ◽  
Amplicon Sequencing ◽  
Positive Sample ◽  
Outdoor Environment ◽  
Rrna Gene ◽  
Total Precipitation ◽  
Monthly Total Precipitation

Abstract Background Legionellosis is caused by the inhalation of aerosolized water contaminated with Legionella bacteria. In this study, we investigated the prevalence of Legionella species in aerosols collected from outdoor sites near asphalt roads, bathrooms in public bath facilities, and other indoor sites, such as buildings and private homes, using amoebic co-culture, quantitative PCR, and 16S rRNA gene amplicon sequencing. Results Legionella species were not detected by amoebic co-culture. However, Legionella DNA was detected in 114/151 (75.5%) air samples collected near roads (geometric mean ± standard deviation: 1.80 ± 0.52 log10 copies/m3), which was comparable to the numbers collected from bathrooms [15/21 (71.4%), 1.82 ± 0.50] but higher than those collected from other indoor sites [11/30 (36.7%), 0.88 ± 0.56] (P < 0.05). The amount of Legionella DNA was correlated with the monthly total precipitation (r = 0.56, P < 0.01). It was also directly and inversely correlated with the daily total precipitation for seven days (r = 0.21, P = 0.01) and one day (r = − 0.29, P < 0.01) before the sampling day, respectively. 16S rRNA gene amplicon sequencing revealed that Legionella species were detected in 9/30 samples collected near roads (mean proportion of reads, 0.11%). At the species level, L. pneumophila was detected in 2/30 samples collected near roads (the proportion of reads, 0.09 and 0.11% of the total reads number in each positive sample). The three most abundant bacterial genera in the samples collected near roads were Sphingomonas, Streptococcus, and Methylobacterium (mean proportion of reads; 21.1%, 14.6%, and 1.6%, respectively). In addition, the bacterial diversity in outdoor environment was comparable to that in indoor environment which contains aerosol-generating features and higher than that in indoor environment without the features. Conclusions DNA from Legionella species was widely present in aerosols collected from outdoor sites near asphalt roads, especially during the rainy season. Our findings suggest that there may be a risk of exposure to Legionella species not only in bathrooms but also in the areas surrounding asphalt roads. Therefore, the possibility of contracting legionellosis in daily life should be considered.

Developing an integrated framework for optimization of spatial requirements of construction equipment

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
S. Hemalattha ◽  
R. Vidjeapriya
Keyword(s):  
Construction Projects ◽  
Indoor Environment ◽  
Outdoor Environment ◽  
Building Information Modelling ◽  
Actual Structure ◽  
Content Type ◽  
Space Planning ◽  
Existing Structures ◽  
Building Information

PurposeThis study aims to develop a framework for optimizing the spatial requirements of the equipment in a construction site using a geographic information system (GIS).Design/methodology/approachAn ongoing construction project, an existing thermal powerplant in India, is considered to be the case study, and the corresponding construction activities were scheduled. The equipment spaces were defined for the scheduled activities in building information modelling (BIM), which was further imported to GIS to define the topology rules, validate and optimize the spatial requirements. The BIM simulates the indoor environment, which includes the actual structure being constructed, and the GIS helps in modelling the outdoor environment, which includes the existing structures, temporary facilitates, topography of the site, etc.; thus, this study incorporates the knowledge of BIM in a geospatial environment to obtain optimized equipment spaces for various activities.FindingsSpace in construction projects is to be considered as a resource as well as a constraint, which is to be modelled and planned according to the requirements. The integration of BIM and GIS for equipment space planning will enable precise identification of the errors in the equipment spaces defined and also result in fewer errors as possible. The integration has also eased the process of assigning the topology rules and validating the same, which otherwise is a tedious process.Originality/valueThe workspace for each activity will include the space of the equipment. But, in most of the previous works of workspace planning, only the labour space is considered, and the conflicts and congestions occurring due to the equipment were neglected. The planning of equipment spaces cannot be done based only on the indoor environment; it has to be carried out by considering the surroundings and topography of the site, which have not been researched extensively despite its importance.

Grid Map Building from Reduced Sonar Data

1998 ◽  
Vol 31 (3) ◽  
pp. 447-452 ◽  
Author(s):  
F. Blanes ◽  
G. Benet ◽  
M. Martínez ◽  
J. Simó
Keyword(s):  
Map Building ◽  
Grid Map

Structural-parametric synthesis of the tracking filter based on decomposition by the target functional with adaptation to trajectory disturbances

2021 ◽  
Author(s):  
A.A. Kostoglotov ◽  
A.S. Penkov ◽  
S.V. Lazarenko
Keyword(s):  
Numerical Modeling ◽  
State Assessment ◽  
Estimation Accuracy ◽  
Motion Model ◽  
Parametric Synthesis ◽  
Adaptive Motion ◽  
Accuracy Criterion ◽  
Dynamic Errors ◽  
Tracking Filter ◽  
Kinematic Motion

Traditional Kalman-type tracking filters are based on a kinematic motion model, which leads to the occurrence of dynamic errors, which significantly increase during target maneuvering. One of the solutions to this problem is to develop a model of motion dynamics with the ability to adapt its structure to external influences. It is shown that the use of a dynamic model of motion in the filter, which takes into account the inertia of the target and the forces acting on it, makes it possible to significantly increase the efficiency of the state assessment. Purpose is to development of an algorithm for assessing the position of a maneuvering object, effective in terms of accuracy criterion. The use of an adaptive motion model as part of the filter provides an increase in the estimation accuracy in comparison with the classical Kalman filter, which is confirmed by the performed numerical modeling.

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