This page has only limited features, please log in for full access.

Unclaimed
Jianga Shang
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430000, China

Basic Info

Basic Info is private.

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 11 May 2021 in Remote Sensing
Reads 0
Downloads 0

Coarse registration of 3D point clouds plays an indispensable role for parametric, semantically rich, and realistic digital twin buildings (DTBs) in the practice of GIScience, manufacturing, robotics, architecture, engineering, and construction. However, the existing methods have prominently been challenged by (i) the high cost of data collection for numerous existing buildings and (ii) the computational complexity from self-similar layout patterns. This paper studies the registration of two low-cost data sets, i.e., colorful 3D point clouds captured by smartphones and 2D CAD drawings, for resolving the first challenge. We propose a novel method named ‘Registration based on Architectural Reflection Detection’ (RegARD) for transforming the self-symmetries in the second challenge from a barrier of coarse registration to a facilitator. First, RegARD detects the innate architectural reflection symmetries to constrain the rotations and reduce degrees of freedom. Then, a nonlinear optimization formulation together with advanced optimization algorithms can overcome the second challenge. As a result, high-quality coarse registration and subsequent low-cost DTBs can be created with semantic components and realistic appearances. Experiments showed that the proposed method outperformed existing methods considerably in both effectiveness and efficiency, i.e., 49.88% less error and 73.13% less time, on average. The RegARD presented in this paper first contributes to coarse registration theories and exploitation of symmetries and textures in 3D point clouds and 2D CAD drawings. For practitioners in the industries, RegARD offers a new automatic solution to utilize ubiquitous smartphone sensors for massive low-cost DTBs.

ACS Style

Yijie Wu; Jianga Shang; Fan Xue. RegARD: Symmetry-Based Coarse Registration of Smartphone’s Colorful Point Clouds with CAD Drawings for Low-Cost Digital Twin Buildings. Remote Sensing 2021, 13, 1882 .

AMA Style

Yijie Wu, Jianga Shang, Fan Xue. RegARD: Symmetry-Based Coarse Registration of Smartphone’s Colorful Point Clouds with CAD Drawings for Low-Cost Digital Twin Buildings. Remote Sensing. 2021; 13 (10):1882.

Chicago/Turabian Style

Yijie Wu; Jianga Shang; Fan Xue. 2021. "RegARD: Symmetry-Based Coarse Registration of Smartphone’s Colorful Point Clouds with CAD Drawings for Low-Cost Digital Twin Buildings." Remote Sensing 13, no. 10: 1882.

Journal article
Published: 23 November 2020 in Sensors
Reads 0
Downloads 0

Map-matching is a popular method that uses spatial information to improve the accuracy of positioning methods. The performance of map matching methods is closely related to spatial characteristics. Although several studies have demonstrated that certain map matching algorithms are affected by some spatial structures (e.g., parallel paths), they focus on the analysis of single map matching method or few spatial structures. In this study, we explored how the most commonly-used four spatial characteristics (namely forks, open spaces, corners, and narrow corridors) affect three popular map matching methods, namely particle filtering (PF), hidden Markov model (HMM), and geometric methods. We first provide a theoretical analysis on how spatial characteristics affect the performance of map matching methods, and then evaluate these effects through experiments. We found that corners and narrow corridors are helpful in improving the positioning accuracy, while forks and open spaces often lead to a larger positioning error. We hope that our findings are helpful for future researchers in choosing proper map matching algorithms with considering the spatial characteristics.

ACS Style

Shuaiwei Luo; Fuqiang Gu; Fan Xu; Jianga Shang. Effect Evaluation of Spatial Characteristics on Map Matching-Based Indoor Positioning. Sensors 2020, 20, 6698 .

AMA Style

Shuaiwei Luo, Fuqiang Gu, Fan Xu, Jianga Shang. Effect Evaluation of Spatial Characteristics on Map Matching-Based Indoor Positioning. Sensors. 2020; 20 (22):6698.

Chicago/Turabian Style

Shuaiwei Luo; Fuqiang Gu; Fan Xu; Jianga Shang. 2020. "Effect Evaluation of Spatial Characteristics on Map Matching-Based Indoor Positioning." Sensors 20, no. 22: 6698.

Research article
Published: 19 August 2020 in Transactions in GIS
Reads 0
Downloads 0

Semantically rich maps are the foundation of indoor location‐based services. Many map providers such as OpenStreetMap and automatic mapping solutions focus on the representation and detection of geometric information (e.g., shape of room) and a few semantics (e.g., stairs and furniture) but neglect room usage. To mitigate the issue, this work proposes a general room tagging method for public buildings, which can benefit both existing map providers and automatic mapping solutions by inferring the missing room usage based on indoor geometric maps. Two kinds of statistical learning‐based room tagging methods are adopted: traditional machine learning (e.g., random forests) and deep learning, specifically relational graph convolutional networks (R‐GCNs), based on the geometric properties (e.g., area), topological relationships (e.g., adjacency and inclusion), and spatial distribution characteristics of rooms. In the machine learning‐based approach, a bidirectional beam search strategy is proposed to deal with the issue that the tag of a room depends on the tag of its neighbors in an undirected room sequence. In the R‐GCN‐based approach, useful properties of neighboring nodes (rooms) in the graph are automatically gathered to classify the nodes. Research buildings are taken as examples to evaluate the proposed approaches based on 130 floor plans with 3,330 rooms by using fivefold cross‐validation. The experiments conducted show that the random forest‐based approach achieves a higher tagging accuracy (0.85) than R‐GCN (0.79).

ACS Style

Xuke Hu; Hongchao Fan; Alexey Noskov; Zhiyong Wang; Alexander Zipf; Fuqiang Gu; Jianga Shang. Room semantics inference using random forest and relational graph convolutional networks: A case study of research building. Transactions in GIS 2020, 25, 71 -111.

AMA Style

Xuke Hu, Hongchao Fan, Alexey Noskov, Zhiyong Wang, Alexander Zipf, Fuqiang Gu, Jianga Shang. Room semantics inference using random forest and relational graph convolutional networks: A case study of research building. Transactions in GIS. 2020; 25 (1):71-111.

Chicago/Turabian Style

Xuke Hu; Hongchao Fan; Alexey Noskov; Zhiyong Wang; Alexander Zipf; Fuqiang Gu; Jianga Shang. 2020. "Room semantics inference using random forest and relational graph convolutional networks: A case study of research building." Transactions in GIS 25, no. 1: 71-111.

Research article
Published: 08 July 2020 in International Journal of Geographical Information Science
Reads 0
Downloads 0

A large proportion of indoor spatial data is generated by parsing floor plans. However, a mature and automatic solution for generating high-quality building elements (e.g., walls and doors) and space partitions (e.g., rooms) is still lacking. In this study, we present a two-stage approach to indoor mapping and modeling (IMM) from floor plan images. The first stage vectorizes the building elements on the floor plan images and the second stage repairs the topological inconsistencies between the building elements, separates indoor spaces, and generates indoor maps and models. To reduce the shape complexity of indoor boundary elements, i.e., walls and openings, we harness the regularity of the boundary elements and extract them as rectangles in the first stage. Furthermore, to resolve the overlaps and gaps of the vectorized results, we propose an optimization model that adjusts the rectangle vertex coordinates to conform to the topological constraints. Experiments demonstrate that our approach achieves a considerable improvement in room detection without conforming to Manhattan World Assumption. Our approach also outputs instance-separate walls with consistent topology, which enables direct modeling into Industry Foundation Classes (IFC) or City Geography Markup Language (CityGML).

ACS Style

Yijie Wu; Jianga Shang; Pan Chen; Sisi Zlatanova; Xuke Hu; Zhiyong Zhou. Indoor mapping and modeling by parsing floor plan images. International Journal of Geographical Information Science 2020, 35, 1205 -1231.

AMA Style

Yijie Wu, Jianga Shang, Pan Chen, Sisi Zlatanova, Xuke Hu, Zhiyong Zhou. Indoor mapping and modeling by parsing floor plan images. International Journal of Geographical Information Science. 2020; 35 (6):1205-1231.

Chicago/Turabian Style

Yijie Wu; Jianga Shang; Pan Chen; Sisi Zlatanova; Xuke Hu; Zhiyong Zhou. 2020. "Indoor mapping and modeling by parsing floor plan images." International Journal of Geographical Information Science 35, no. 6: 1205-1231.

Journal article
Published: 22 April 2020 in IEEE Internet of Things Journal
Reads 0
Downloads 0

Indoor localization is important for a variety of applications such as location-based services, mobile social networks, and emergency response. Fusing spatial information is an effective way to achieve accurate indoor localization with little or with no need for extra hardware. However, existing indoor localization methods that make use of spatial information are either computationally expensive or sensitive to the completeness of landmarks. In this paper, we propose a novel, low-cost, high-accuracy indoor localization method based on a landmark graph. Experimental results show that the proposed method outperforms the state-of-the-art methods.

ACS Style

Fuqiang Gu; Shahrokh Valaee; Kourosh Khoshelham; Jianga Shang; Rui Zhang. Landmark Graph-Based Indoor Localization. IEEE Internet of Things Journal 2020, 7, 8343 -8355.

AMA Style

Fuqiang Gu, Shahrokh Valaee, Kourosh Khoshelham, Jianga Shang, Rui Zhang. Landmark Graph-Based Indoor Localization. IEEE Internet of Things Journal. 2020; 7 (9):8343-8355.

Chicago/Turabian Style

Fuqiang Gu; Shahrokh Valaee; Kourosh Khoshelham; Jianga Shang; Rui Zhang. 2020. "Landmark Graph-Based Indoor Localization." IEEE Internet of Things Journal 7, no. 9: 8343-8355.

Journal article
Published: 19 April 2020 in ISPRS International Journal of Geo-Information
Reads 0
Downloads 0

Wi-Fi fingerprinting has been widely used for indoor localization because of its good cost-effectiveness. However, it suffers from relatively low localization accuracy and robustness owing to the signal fluctuations. Virtual Access Points (VAP) can effectively reduce the impact of signal fluctuation problem in Wi-Fi fingerprinting. Current techniques normally use the Log-Normal Shadowing Model to estimate the virtual location of the access point. This would lead to inaccurate location estimation due to the signal attenuation factor in the model, which is difficult to be determined. To overcome this challenge, in this study, we propose a novel approach to calculating the virtual location of the access points by using the Apollonius Circle theory, specifically the distance ratio, which can eliminate the attenuation parameter term in the original model. This is based on the assumption that neighboring locations share the same attenuation parameter corresponding to the signal attenuation caused by obstacles. We evaluated the proposed method in a laboratory building with three different kinds of scenes and 1194 test points in total. The experimental results show that the proposed approach can improve the accuracy and robustness of the Wi-Fi fingerprinting techniques and achieve state-of-art performance.

ACS Style

Fan Xu; Xuke Hu; Shuaiwei Luo; Jianga Shang. An Efficient Indoor Wi-Fi Positioning Method Using Virtual Location of AP. ISPRS International Journal of Geo-Information 2020, 9, 261 .

AMA Style

Fan Xu, Xuke Hu, Shuaiwei Luo, Jianga Shang. An Efficient Indoor Wi-Fi Positioning Method Using Virtual Location of AP. ISPRS International Journal of Geo-Information. 2020; 9 (4):261.

Chicago/Turabian Style

Fan Xu; Xuke Hu; Shuaiwei Luo; Jianga Shang. 2020. "An Efficient Indoor Wi-Fi Positioning Method Using Virtual Location of AP." ISPRS International Journal of Geo-Information 9, no. 4: 261.

Journal article
Published: 31 January 2020 in Future Generation Computer Systems
Reads 0
Downloads 0

Map matching is a commonly-used technique that employs spatial constraints to improve positioning results. While map matching can improve the positioning performance to a large extent, existing map matching methods consider only adjacent transitions between reference points (RPs). This makes these map matching methods depend highly on the sampling size of RPs. To reduce the influence of the RPs’ sampling size, a novel map matching method called PDMatching is proposed in this paper, which considers both adjacent and non-adjacent transitions. These transitions are described based on the path distance of the RP sequence obtained by the shortest path algorithm. Compared to the commonly-used Euclidean distance, the path distance is more suitable for map matching as it takes into account spatial constraints. It allows to estimate the transition distance more accurately, which can further improve the positioning accuracy. To infer the location of a user, the student’s t-distribution is used to transform the path distance into a transition probability, from which the location can be obtained via the Viterbi algorithm. Extensive experiments have been conducted to evaluate the proposed PDMatching in a large museum environment. Experimental results show that the proposed PDMatching can achieve a mean localization error of 3.4m and 4.6m for uniform and varying speed modes, respectively, which outperforms the state-of-the-art methods (e.g., MapCraft, VTrack, XINS). Moreover, the PDMatching is more robust to the sampling size of RPs than other methods.

ACS Style

Pan Chen; Xiaoping Zhengac; Fuqiang Gub; Jianga Shangac. Path distance-based map matching for Wi-Fi fingerprinting positioning. Future Generation Computer Systems 2020, 107, 82 -94.

AMA Style

Pan Chen, Xiaoping Zhengac, Fuqiang Gub, Jianga Shangac. Path distance-based map matching for Wi-Fi fingerprinting positioning. Future Generation Computer Systems. 2020; 107 ():82-94.

Chicago/Turabian Style

Pan Chen; Xiaoping Zhengac; Fuqiang Gub; Jianga Shangac. 2020. "Path distance-based map matching for Wi-Fi fingerprinting positioning." Future Generation Computer Systems 107, no. : 82-94.

Research article
Published: 04 January 2020 in International Journal of Digital Earth
Reads 0
Downloads 0

In landmark-based way-finding, determining the most salient landmark from several candidates at decision points is challenging. To overcome this problem, current approaches usually rely on a linear model to measure the salience of landmarks. However, linear models are not always able to establish an accurate quantitative relationship between the attributes of a landmark and its perceived salience. Furthermore, the numbers of evaluated scenes and of volunteers participating in the testing of these models are often limited. With the aim of overcoming these gaps, we propose learning a non-linear salience model by means of genetic programming. We compared our proposed approach with conventional algorithms by using photographs of two hundred test scenes collected from two shopping malls. Two hundred volunteers who were not in these environments were asked to answer questionnaires about the collected photographs. The results from this experiment showed that in 76% of the cases, the most salient landmark (according to the volunteers' perception) was correctly predicted by our proposed approach. This accuracy rate is considerably higher than the ones achieved by conventional linear models.

ACS Style

Xuke Hu; Lei Ding; Jianga Shang; Hongchao Fan; Tessio Novack; Alexey Noskov; Alexander Zipf. Data-driven approach to learning salience models of indoor landmarks by using genetic programming. International Journal of Digital Earth 2020, 13, 1230 -1257.

AMA Style

Xuke Hu, Lei Ding, Jianga Shang, Hongchao Fan, Tessio Novack, Alexey Noskov, Alexander Zipf. Data-driven approach to learning salience models of indoor landmarks by using genetic programming. International Journal of Digital Earth. 2020; 13 (11):1230-1257.

Chicago/Turabian Style

Xuke Hu; Lei Ding; Jianga Shang; Hongchao Fan; Tessio Novack; Alexey Noskov; Alexander Zipf. 2020. "Data-driven approach to learning salience models of indoor landmarks by using genetic programming." International Journal of Digital Earth 13, no. 11: 1230-1257.

Journal article
Published: 12 December 2019 in IEEE Transactions on Vehicular Technology
Reads 0
Downloads 0

Wi-Fi fingerprinting is widely used in indoor localization due to the ubiquitous availability of Wi-Fi infrastructure in indoor environments. The basic assumption of fingerprinting localization is that the received signal strength indication (RSSI) distance is consistent with the location distance. However, due to the fluctuation of Wi-Fi signals in indoor environments, the nearest neighbors selected using the RSSI distance may not be those whose corresponding locations are nearest to the target, which could lead to a large localization error. In this paper, we propose a novel fingerprinting method for indoor localization by transforming raw RSSI into features with a learned non-linear mapping function. To learn such mapping function, we design a triple loss function that measures the difference between the rank of RSSI distance and that of location distance. By minimizing the loss function iteratively, we can learn the non-linear mapping function with the gradient boosting regression forest (GBRF) method. Experiments have been conducted in a complex environment and experimental results show that our method outperforms the state-of-the-art methods.

ACS Style

Pan Chen; Jianga Shang; Fuqiang Gu. Learning RSSI Feature via Ranking Model for Wi-Fi Fingerprinting Localization. IEEE Transactions on Vehicular Technology 2019, 69, 1695 -1705.

AMA Style

Pan Chen, Jianga Shang, Fuqiang Gu. Learning RSSI Feature via Ranking Model for Wi-Fi Fingerprinting Localization. IEEE Transactions on Vehicular Technology. 2019; 69 (2):1695-1705.

Chicago/Turabian Style

Pan Chen; Jianga Shang; Fuqiang Gu. 2019. "Learning RSSI Feature via Ranking Model for Wi-Fi Fingerprinting Localization." IEEE Transactions on Vehicular Technology 69, no. 2: 1695-1705.

Original article
Published: 30 September 2019 in Biological Cybernetics
Reads 0
Downloads 0

Roboticists have long drawn inspiration from nature to develop navigation and simultaneous localization and mapping (SLAM) systems such as RatSLAM. Animals such as birds and bats possess superlative navigation capabilities, robustly navigating over large, three-dimensional environments, leveraging an internal neural representation of space combined with external sensory cues and self-motion cues. This paper presents a novel neuro-inspired 4DoF (degrees of freedom) SLAM system named NeuroSLAM, based upon computational models of 3D grid cells and multilayered head direction cells, integrated with a vision system that provides external visual cues and self-motion cues. NeuroSLAM’s neural network activity drives the creation of a multilayered graphical experience map in a real time, enabling relocalization and loop closure through sequences of familiar local visual cues. A multilayered experience map relaxation algorithm is used to correct cumulative errors in path integration after loop closure. Using both synthetic and real-world datasets comprising complex, multilayered indoor and outdoor environments, we demonstrate NeuroSLAM consistently producing topologically correct three-dimensional maps.

ACS Style

Fangwen Yu; Jianga Shang; Youjian Hu; Michael Milford. NeuroSLAM: a brain-inspired SLAM system for 3D environments. Biological Cybernetics 2019, 113, 515 -545.

AMA Style

Fangwen Yu, Jianga Shang, Youjian Hu, Michael Milford. NeuroSLAM: a brain-inspired SLAM system for 3D environments. Biological Cybernetics. 2019; 113 (5-6):515-545.

Chicago/Turabian Style

Fangwen Yu; Jianga Shang; Youjian Hu; Michael Milford. 2019. "NeuroSLAM: a brain-inspired SLAM system for 3D environments." Biological Cybernetics 113, no. 5-6: 515-545.

Journal article
Published: 28 June 2019 in Remote Sensing
Reads 0
Downloads 0

Current indoor mapping approaches can detect accurate geometric information but are incapable of detecting the room type or dismiss this issue. This work investigates the feasibility of inferring the room type by using grammars based on geometric maps. Specifically, we take the research buildings at universities as examples and create a constrained attribute grammar to represent the spatial distribution characteristics of different room types as well as the topological relations among them. Based on the grammar, we propose a bottom-up approach to construct a parse forest and to infer the room type. During this process, Bayesian inference method is used to calculate the initial probability of belonging an enclosed room to a certain type given its geometric properties (e.g., area, length, and width) that are extracted from the geometric map. The approach was tested on 15 maps with 408 rooms. In 84% of cases, room types were defined correctly. It, to a certain degree, proves that grammars can benefit semantic enrichment (in particular, room type tagging).

ACS Style

Xuke Hu; Hongchao Fan; Alexey Noskov; Alexander Zipf; Zhiyong Wang; Jianga Shang. Feasibility of Using Grammars to Infer Room Semantics. Remote Sensing 2019, 11, 1535 .

AMA Style

Xuke Hu, Hongchao Fan, Alexey Noskov, Alexander Zipf, Zhiyong Wang, Jianga Shang. Feasibility of Using Grammars to Infer Room Semantics. Remote Sensing. 2019; 11 (13):1535.

Chicago/Turabian Style

Xuke Hu; Hongchao Fan; Alexey Noskov; Alexander Zipf; Zhiyong Wang; Jianga Shang. 2019. "Feasibility of Using Grammars to Infer Room Semantics." Remote Sensing 11, no. 13: 1535.

Journal article
Published: 07 January 2019 in IEEE Sensors Journal
Reads 0
Downloads 0

Indoor localization has become a hot topic in recent years because of its wide applications. Map matching is a popular method used to improve the localization accuracy without adding hardware. However, existing map matching methods are usually computationally expensive, leading to the unsuitability of running on resource-limited devices such as smartphones. In this paper, we present an efficient map matching system for indoor localization, called HTrack, which uses a hidden Markov model with considering user’s heading and spatial information. By considering user’s heading information, we significantly reduce the number of candidate states for each step and hence improve the computational efficiency. Experimental results show that HTrack outperforms the state-of-the-art methods (more than 25% localization accuracy improvement), and consumes about 5 times less energy than the state-of-the-art methods.

ACS Style

Yongfeng Wu; Pan Chen; Fuqiang Gu; Xiaoping Zheng; Jianga Shang. $HTrack$ : An Efficient Heading-Aided Map Matching for Indoor Localization and Tracking. IEEE Sensors Journal 2019, 19, 3100 -3110.

AMA Style

Yongfeng Wu, Pan Chen, Fuqiang Gu, Xiaoping Zheng, Jianga Shang. $HTrack$ : An Efficient Heading-Aided Map Matching for Indoor Localization and Tracking. IEEE Sensors Journal. 2019; 19 (8):3100-3110.

Chicago/Turabian Style

Yongfeng Wu; Pan Chen; Fuqiang Gu; Xiaoping Zheng; Jianga Shang. 2019. "$HTrack$ : An Efficient Heading-Aided Map Matching for Indoor Localization and Tracking." IEEE Sensors Journal 19, no. 8: 3100-3110.

Journal article
Published: 10 October 2018 in IEEE Transactions on Instrumentation and Measurement
Reads 0
Downloads 0

Pedestrian dead reckoning (PDR) is a popular indoor localization method due to its independence of additional infrastructures and the wide availability of smart devices. Step length estimation is a key component of PDR, which has an important influence on the performance of PDR. Existing step length estimation models suffer from various limitations such as requiring knowledge of user's height, lack of consideration of varying phone carrying ways, and dependence on spatial constraints. To solve these problems, we propose a deep learning-based step length estimation model, which can adapt to different phone carrying ways and does not require individual stature information and spatial constraints. Experimental results show that the proposed method outperforms existing popular step length estimation methods.

ACS Style

Fuqiang Gu; Kourosh Khoshelham; Chunyang Yu; Jianga Shang. Accurate Step Length Estimation for Pedestrian Dead Reckoning Localization Using Stacked Autoencoders. IEEE Transactions on Instrumentation and Measurement 2018, 68, 2705 -2713.

AMA Style

Fuqiang Gu, Kourosh Khoshelham, Chunyang Yu, Jianga Shang. Accurate Step Length Estimation for Pedestrian Dead Reckoning Localization Using Stacked Autoencoders. IEEE Transactions on Instrumentation and Measurement. 2018; 68 (8):2705-2713.

Chicago/Turabian Style

Fuqiang Gu; Kourosh Khoshelham; Chunyang Yu; Jianga Shang. 2018. "Accurate Step Length Estimation for Pedestrian Dead Reckoning Localization Using Stacked Autoencoders." IEEE Transactions on Instrumentation and Measurement 68, no. 8: 2705-2713.

Journal article
Published: 04 April 2018 in IEEE Internet of Things Journal
Reads 0
Downloads 0

Locomotion activity recognition (LAR) is important for a number of applications, such as indoor localization, fitness tracking, and aged care. Existing methods usually use handcrafted features, which requires expert knowledge and is laborious, and the achieved result might still be suboptimal. To relieve the burden of designing and selecting features, we propose a deep learning method for LAR by using data from multiple sensors available on most smart devices. Experimental results show that the proposed method, which learns useful features automatically, outperforms conventional classifiers that require the hand-engineering of features. We also show that the combination of sensor data from four sensors (accelerometer, gyroscope, magnetometer, and barometer) achieves a higher accuracy than other combinations or individual sensors.

ACS Style

Fuqiang Gu; Kourosh Khoshelham; Shahrokh Valaee; Jianga Shang; Rui Zhang. Locomotion Activity Recognition Using Stacked Denoising Autoencoders. IEEE Internet of Things Journal 2018, 5, 2085 -2093.

AMA Style

Fuqiang Gu, Kourosh Khoshelham, Shahrokh Valaee, Jianga Shang, Rui Zhang. Locomotion Activity Recognition Using Stacked Denoising Autoencoders. IEEE Internet of Things Journal. 2018; 5 (3):2085-2093.

Chicago/Turabian Style

Fuqiang Gu; Kourosh Khoshelham; Shahrokh Valaee; Jianga Shang; Rui Zhang. 2018. "Locomotion Activity Recognition Using Stacked Denoising Autoencoders." IEEE Internet of Things Journal 5, no. 3: 2085-2093.

Journal article
Published: 14 September 2017 in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Reads 0
Downloads 0

Maps are the foundation of indoor location-based services. Many automatic indoor mapping approaches have been proposed, but they rely highly on sensor data, such as point clouds and users’ location traces. To address this issue, this paper presents a conceptual framework to represent the layout principle of research buildings by using grammars. This framework can benefit the indoor mapping process by improving the accuracy of generated maps and by dramatically reducing the volume of the sensor data required by traditional reconstruction approaches. In addition, we try to present more details of partial core modules of the framework. An example using the proposed framework is given to show the generation process of a semantic map. This framework is part of an ongoing research for the development of an approach for reconstructing semantic maps.

ACS Style

X. Hu; H. Fan; Alexander Zipf; J. Shang; Fuqiang Gu. A CONCEPTUAL FRAMEWORK FOR INDOOR MAPPING BY USING GRAMMARS. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2017, IV-2/W4, 335 -342.

AMA Style

X. Hu, H. Fan, Alexander Zipf, J. Shang, Fuqiang Gu. A CONCEPTUAL FRAMEWORK FOR INDOOR MAPPING BY USING GRAMMARS. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2017; IV-2/W4 ():335-342.

Chicago/Turabian Style

X. Hu; H. Fan; Alexander Zipf; J. Shang; Fuqiang Gu. 2017. "A CONCEPTUAL FRAMEWORK FOR INDOOR MAPPING BY USING GRAMMARS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W4, no. : 335-342.

Journal article
Published: 15 December 2016 in Sensors
Reads 0
Downloads 0

Although map filtering-aided Pedestrian Dead Reckoning (PDR) is capable of largely improving indoor localization accuracy, it becomes less efficient when coping with highly complex indoor spaces. For instance, indoor spaces with a few close corners or neighboring passages can lead to particles entering erroneous passages, which can further cause the failure of subsequent tracking. To address this problem, we propose GridiLoc, a reliable and accurate pedestrian indoor localization method through the fusion of smartphone sensors and a grid model. The key novelty of GridiLoc is the utilization of a backtracking grid filter for improving localization accuracy and for handling dead ending issues. In order to reduce the time consumption of backtracking, a topological graph is introduced for representing candidate backtracking points, which are the expected locations at the starting time of the dead ending. Furthermore, when the dead ending is caused by the erroneous step length model of PDR, our solution can automatically calibrate the model by using the historical tracking data. Our experimental results show that GridiLoc achieves a higher localization accuracy and reliability compared with the commonly-used map filtering approach. Meanwhile, it maintains an acceptable computational complexity.

ACS Style

Jianga Shang; Xuke Hu; Wen Cheng; Hongchao Fan. GridiLoc: A Backtracking Grid Filter for Fusing the Grid Model with PDR Using Smartphone Sensors. Sensors 2016, 16, 2137 .

AMA Style

Jianga Shang, Xuke Hu, Wen Cheng, Hongchao Fan. GridiLoc: A Backtracking Grid Filter for Fusing the Grid Model with PDR Using Smartphone Sensors. Sensors. 2016; 16 (12):2137.

Chicago/Turabian Style

Jianga Shang; Xuke Hu; Wen Cheng; Hongchao Fan. 2016. "GridiLoc: A Backtracking Grid Filter for Fusing the Grid Model with PDR Using Smartphone Sensors." Sensors 16, no. 12: 2137.

Journal article
Published: 08 June 2016 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Reads 0
Downloads 0

Indoor localization is important for a variety of applications such as location-based services, mobile social networks, and emergency response. Fusing spatial information is an effective way to achieve accurate indoor localization with little or with no need for extra hardware. However, existing indoor localization methods that make use of spatial information are either too computationally expensive or too sensitive to the completeness of landmark detection. In this paper, we solve this problem by using the proposed landmark graph. The landmark graph is a directed graph where nodes are landmarks (e.g., doors, staircases, and turns) and edges are accessible paths with heading information. We compared the proposed method with two common Dead Reckoning (DR)-based methods (namely, Compass + Accelerometer + Landmarks and Gyroscope + Accelerometer + Landmarks) by a series of experiments. Experimental results show that the proposed method can achieve 73% accuracy with a positioning error less than 2.5 meters, which outperforms the other two DR-based methods.

ACS Style

Fuqiang Gu; A. Kealy; K. Khoshelham; J. Shang. EFFICIENT AND ACCURATE INDOOR LOCALIZATION USING LANDMARK GRAPHS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2016, XLI-B2, 509 -514.

AMA Style

Fuqiang Gu, A. Kealy, K. Khoshelham, J. Shang. EFFICIENT AND ACCURATE INDOOR LOCALIZATION USING LANDMARK GRAPHS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2016; XLI-B2 ():509-514.

Chicago/Turabian Style

Fuqiang Gu; A. Kealy; K. Khoshelham; J. Shang. 2016. "EFFICIENT AND ACCURATE INDOOR LOCALIZATION USING LANDMARK GRAPHS." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2, no. : 509-514.

Journal article
Published: 04 December 2015 in Sensors
Reads 0
Downloads 0

The recognition of locomotion activities (e.g., walking, running, still) is important for a wide range of applications like indoor positioning, navigation, location-based services, and health monitoring. Recently, there has been a growing interest in activity recognition using accelerometer data. However, when utilizing only acceleration-based features, it is difficult to differentiate varying vertical motion states from horizontal motion states especially when conducting user-independent classification. In this paper, we also make use of the newly emerging barometer built in modern smartphones, and propose a novel feature called pressure derivative from the barometer readings for user motion state recognition, which is proven to be effective for distinguishing vertical motion states and does not depend on specific users’ data. Seven types of motion states are defined and six commonly-used classifiers are compared. In addition, we utilize the motion state history and the characteristics of people’s motion to improve the classification accuracies of those classifiers. Experimental results show that by using the historical information and human’s motion characteristics, we can achieve user-independent motion state classification with an accuracy of up to 90.7%. In addition, we analyze the influence of the window size and smartphone pose on the accuracy.

ACS Style

Fuqiang Gu; Allison Kealy; Kourosh Khoshelham; Jianga Shang. User-Independent Motion State Recognition Using Smartphone Sensors. Sensors 2015, 15, 30636 -30652.

AMA Style

Fuqiang Gu, Allison Kealy, Kourosh Khoshelham, Jianga Shang. User-Independent Motion State Recognition Using Smartphone Sensors. Sensors. 2015; 15 (12):30636-30652.

Chicago/Turabian Style

Fuqiang Gu; Allison Kealy; Kourosh Khoshelham; Jianga Shang. 2015. "User-Independent Motion State Recognition Using Smartphone Sensors." Sensors 15, no. 12: 30636-30652.

Journal article
Published: 26 October 2015 in Sensors
Reads 0
Downloads 0

The utility and adoption of indoor localization applications have been limited due to the complex nature of the physical environment combined with an increasing requirement for more robust localization performance. Existing solutions to this problem are either too expensive or too dependent on infrastructure such as Wi-Fi access points. To address this problem, we propose APFiLoc—a low cost, smartphone-based framework for indoor localization. The key idea behind this framework is to obtain landmarks within the environment and to use the augmented particle filter to fuse them with measurements from smartphone sensors and map information. A clustering method based on distance constraints is developed to detect organic landmarks in an unsupervised way, and the least square support vector machine is used to classify seed landmarks. A series of real-world experiments were conducted in complex environments including multiple floors and the results show APFiLoc can achieve 80% accuracy (phone in the hand) and around 70% accuracy (phone in the pocket) of the error less than 2 m error without the assistance of infrastructure like Wi-Fi access points.

ACS Style

Jianga Shang; Fuqiang Gu; Xuke Hu; Allison Kealy. APFiLoc: An Infrastructure-Free Indoor Localization Method Fusing Smartphone Inertial Sensors, Landmarks and Map Information. Sensors 2015, 15, 27251 -27272.

AMA Style

Jianga Shang, Fuqiang Gu, Xuke Hu, Allison Kealy. APFiLoc: An Infrastructure-Free Indoor Localization Method Fusing Smartphone Inertial Sensors, Landmarks and Map Information. Sensors. 2015; 15 (10):27251-27272.

Chicago/Turabian Style

Jianga Shang; Fuqiang Gu; Xuke Hu; Allison Kealy. 2015. "APFiLoc: An Infrastructure-Free Indoor Localization Method Fusing Smartphone Inertial Sensors, Landmarks and Map Information." Sensors 15, no. 10: 27251-27272.

Review article
Published: 05 May 2015 in Mathematical Problems in Engineering
Reads 0
Downloads 0

Location estimation is significant in mobile and ubiquitous computing systems. The complexity and smaller scale of the indoor environment impose a great impact on location estimation. The key of location estimation lies in the representation and fusion of uncertain information from multiple sources. The improvement of location estimation is a complicated and comprehensive issue. A lot of research has been done to address this issue. However, existing research typically focuses on certain aspects of the problem and specific methods. This paper reviews mainstream schemes on improving indoor location estimation from multiple levels and perspectives by combining existing works and our own working experiences. Initially, we analyze the error sources of common indoor localization techniques and provide a multilayered conceptual framework of improvement schemes for location estimation. This is followed by a discussion of probabilistic methods for location estimation, including Bayes filters, Kalman filters, extended Kalman filters, sigma-point Kalman filters, particle filters, and hidden Markov models. Then, we investigate the hybrid localization methods, including multimodal fingerprinting, triangulation fusing multiple measurements, combination of wireless positioning with pedestrian dead reckoning (PDR), and cooperative localization. Next, we focus on the location determination approaches that fuse spatial contexts, namely, map matching, landmark fusion, and spatial model-aided methods. Finally, we present the directions for future research.

ACS Style

Jianga Shang; Xuke Hu; Fuqiang Gu; Di Wang; Shengsheng Yu. Improvement Schemes for Indoor Mobile Location Estimation: A Survey. Mathematical Problems in Engineering 2015, 2015, 1 -32.

AMA Style

Jianga Shang, Xuke Hu, Fuqiang Gu, Di Wang, Shengsheng Yu. Improvement Schemes for Indoor Mobile Location Estimation: A Survey. Mathematical Problems in Engineering. 2015; 2015 ():1-32.

Chicago/Turabian Style

Jianga Shang; Xuke Hu; Fuqiang Gu; Di Wang; Shengsheng Yu. 2015. "Improvement Schemes for Indoor Mobile Location Estimation: A Survey." Mathematical Problems in Engineering 2015, no. : 1-32.