scholarly journals TWO NEW PEDESTRIAN NAVIGATION PATH OPTIONS BASED ON SEMI-INDOOR SPACE

2020 ◽  
Vol VI-4/W1-2020 ◽  
pp. 175-182
Author(s):  
J. Yan ◽  
S. Zlatanova ◽  
A. A. Diakite
Keyword(s):  
Path Selection ◽  
Selection Strategy ◽  
Navigation Systems ◽  
Indoor Space ◽  
Pedestrian Navigation ◽  
Navigation Path ◽  
Shortest Distance ◽  
Making Choices ◽  
Navigation Experience ◽  
Indoor Spaces

Abstract. Navigation is very critical for our daily life, especially when we have to go through the unfamiliar areas where the spaces are very complex, such as completely bounded (indoor), partially bounded (semi-indoor and/or semi-outdoor), entirely open (outdoor), or combined. Current navigation systems commonly offer the shortest distance/time path, but it is not always appropriate for some situations. For instance, on a rainy day, a path with as many places that are covered by roofs/shelters is more attractive. However, current navigation systems cannot provide such kinds of navigation paths, which can be explained by that they lack information about such roofed/sheltered-covered spaces. This paper proposes two roofed/sheltered navigation path options by employing semi-indoor spaces in the navigation map: (i) the Most-Top-Covered path (MTC-path) and (ii) path to the Nearest sI-space from departure (NSI-path). A path selection strategy is introduced to help pedestrians in making choices between the two new path options and the traditional shortest path. We demonstrate and validate the research with path planning on two navigation cases. The results show the two path options and the path selection strategy bring in new navigation experience for humans.

scholarly journals Indoor Routing on Logical Network Using Space Semantics

2019 ◽  
Vol 8 (3) ◽  
pp. 126 ◽  
Author(s):  
Liu Liu ◽  
Sisi Zlatanova ◽  
Bofeng Li ◽  
Peter van Oosterom ◽  
Hua Liu ◽  
...  
Keyword(s):  
Path Selection ◽  
Geometric Information ◽  
Logical Network ◽  
Navigation Path ◽  
Path Computation ◽  
Routing Mechanism ◽  
Routing Method ◽  
Priority Ordering ◽  
Routing Methods ◽  
Indoor Spaces

An indoor logical network qualitatively represents abstract relationships between indoor spaces, and it can be used for path computation. In this paper, we concentrate on the logical network that does not have notions for metrics. Instead, it relies on the semantics and properties of indoor spaces. A navigation path can be computed by deriving parameters from these semantics and minimizing them in routing algorithms. Although previous studies have adopted semantic approaches to build logical networks, routing methods are seldom elaborated. The main issue with such networks is to derive criteria for path computation using the semantics of spaces. Here, we present a routing mechanism that is based on a dedicated space classification and a set of routing criteria. The space classification reflects characteristics of spaces that are important for navigation, such as horizontal and vertical directions, doors and windows, etc. Six routing criteria are introduced, and they involve: (1) the spaces with the preferred semantics; and/or (2) their centrality in the logical network. Each criterion is encoded as the weights to the nodes or edges of the logical network by considering the semantics of spaces. Logical paths are derived by a traditional shortest-path algorithm that minimizes these weights. Depending on the building’s interior configuration, one criterion may result in several logical paths. Therefore, we introduce a priority ordering of criteria to support path selection and decrease the possible number of logical paths. We provide a proof-of-concept implementation for several buildings to demonstrate the usability of such a routing. The main benefit of this routing method is that it does not need geometric information to compute a path. The logical network can be created using verbal descriptions only, and this routing method can be applied to indoor spaces derived from any building subdivision.

scholarly journals Top-Bounded Spaces Formed by the Built Environment for Navigation Systems

2019 ◽  
Vol 8 (5) ◽  
pp. 224 ◽  
Author(s):  
Jinjin Yan ◽  
Abdoulaye A. Diakité ◽  
Sisi Zlatanova ◽  
Mitko Aleksandrov
Keyword(s):  
Built Environment ◽  
Optimal Path ◽  
Use Case ◽  
Navigation Systems ◽  
Space Model ◽  
Navigation Path ◽  
Shortest Distance ◽  
Systems Models ◽  
The Right

Navigation systems help agents find the right (optimal) path from the origin to the desired destination. Current navigation systems mainly offer the shortest (distance or time) path as the default optimal path. However, under certain circumstances, having a least-top-exposed path can be more interesting. For instance, on a rainy day, a path with as many places as possible covered by roofs/shelters is more attractive and pragmatic, since roofs/shelters can offer protection from rain. In this paper, we name environments that covered by roofs/shelters but not completely enclosed like indoors as “top-bounded environments/spaces” (e.g., porches), which are generally formed by built structures. This kind of space is completely missing in current navigation models and systems. Thus, we investigate how to use it for space-based navigation. After proposing a definition, a space model, and attributes of top-bounded spaces, we introduce a projection-based approach to generate them. Then, taking a pedestrian as an example agent, we select generated spaces considering whether the agent can visit/use the identified spaces. Finally, examples and a use case study demonstrate that our research can help to include top-bounded spaces in navigation systems/models. More navigation path types (e.g., least-top-exposed) can be offered for different agents (such as pedestrians, drones or robots).

scholarly journals Indoor Traveling Salesman Problem (ITSP) Path Planning

2021 ◽  
Vol 10 (9) ◽  
pp. 616
Author(s):  
Jinjin Yan ◽  
Sisi Zlatanova ◽  
Jinwoo (Brian) Lee ◽  
Qingxiang Liu
Keyword(s):  
Path Planning ◽  
Traveling Salesman Problem ◽  
Indoor Navigation ◽  
Traveling Salesman ◽  
Shopping Mall ◽  
Navigation Systems ◽  
Quick Response ◽  
Navigation Path ◽  
Shortest Distance ◽  
Increasing Demand

With the growing complexity of indoor living environments, people have an increasing demand for indoor navigation. Currently, navigation path options in indoor are monotonous as existing navigation systems commonly offer single-source shortest-distance or fastest paths. Such path options might be not always attractive. For instance, pedestrians in a shopping mall may be interested in a path that navigates through multiple places starting from and ending at the same location. Here, we name it as the indoor traveling salesman problem (ITSP) path. As its name implies, this path type is similar to the classical outdoor traveling salesman problem (TSP), namely, the shortest path that visits a number of places exactly once and returns to the original departure place. This paper presents a general solution to the ITSP path based on Dijkstra and branch and bound (B&B) algorithm. We demonstrate and validate the method by applying it to path planning in a large shopping mall with six floors, in which the QR (Quick Response) codes are assumed to be utilized as the indoor positioning approach. The results show that the presented solution can successfully compute the ITSP paths and their potentials to apply to other indoor navigation applications at museums or hospitals.

scholarly journals Smartphone-Based Activity Recognition in a Pedestrian Navigation Context

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3243
Author(s):  
Robert Jackermeier ◽  
Bernd Ludwig
Keyword(s):  
Activity Recognition ◽  
Recognition Performance ◽  
Activity Patterns ◽  
Detailed Knowledge ◽  
Navigation Systems ◽  
Pedestrian Navigation ◽  
User Activity ◽  
Device Placement ◽  
Locomotion Behavior ◽  
Current Context

In smartphone-based pedestrian navigation systems, detailed knowledge about user activity and device placement is a key information. Landmarks such as staircases or elevators can help the system in determining the user position when located inside buildings, and navigation instructions can be adapted to the current context in order to provide more meaningful assistance. Typically, most human activity recognition (HAR) approaches distinguish between general activities such as walking, standing or sitting. In this work, we investigate more specific activities that are tailored towards the use-case of pedestrian navigation, including different kinds of stationary and locomotion behavior. We first collect a dataset of 28 combinations of device placements and activities, in total consisting of over 6 h of data from three sensors. We then use LSTM-based machine learning (ML) methods to successfully train hierarchical classifiers that can distinguish between these placements and activities. Test results show that the accuracy of device placement classification (97.2%) is on par with a state-of-the-art benchmark in this dataset while being less resource-intensive on mobile devices. Activity recognition performance highly depends on the classification task and ranges from 62.6% to 98.7%, once again performing close to the benchmark. Finally, we demonstrate in a case study how to apply the hierarchical classifiers to experimental and naturalistic datasets in order to analyze activity patterns during the course of a typical navigation session and to investigate the correlation between user activity and device placement, thereby gaining insights into real-world navigation behavior.

scholarly journals A Hybrid Framework for Mitigating Heading Drift for a Wearable Pedestrian Navigation System through Adaptive Fusion of Inertial and Magnetic Measurements

2021 ◽  
Vol 11 (4) ◽  
pp. 1902
Author(s):  
Liqiang Zhang ◽  
Yu Liu ◽  
Jinglin Sun
Keyword(s):  
Magnetic Measurements ◽  
Inertial Sensor ◽  
Angular Rate ◽  
Human Motion ◽  
Navigation Systems ◽  
Pedestrian Navigation ◽  
Hybrid Framework ◽  
Outdoor Environments ◽  
Adaptive Fusion ◽  
Indoor And Outdoor Environments

Pedestrian navigation systems could serve as a good supplement for other navigation methods or for extending navigation into areas where other navigation systems are invalid. Due to the accumulation of inertial sensing errors, foot-mounted inertial-sensor-based pedestrian navigation systems (PNSs) suffer from drift, especially heading drift. To mitigate heading drift, considering the complexity of human motion and the environment, we introduce a novel hybrid framework that integrates a foot-state classifier that triggers the zero-velocity update (ZUPT) algorithm, zero-angular-rate update (ZARU) algorithm, and a state lock, a magnetic disturbance detector, a human-motion-classifier-aided adaptive fusion module (AFM) that outputs an adaptive heading error measurement by fusing heuristic and magnetic algorithms rather than simply switching them, and an error-state Kalman filter (ESKF) that estimates the optimal systematic error. The validation datasets include a Vicon loop dataset that spans 324.3 m in a single room for approximately 300 s and challenging walking datasets that cover large indoor and outdoor environments with a total distance of 12.98 km. A total of five different frameworks with different heading drift correction methods, including the proposed framework, were validated on these datasets, which demonstrated that our proposed ZUPT–ZARU–AFM–ESKF-aided PNS outperforms other frameworks and clearly mitigates heading drift.

scholarly journals Automatic Reconstruction of Multi-Level Indoor Spaces from Point Cloud and Trajectory

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3493
Author(s):  
Gahyeon Lim ◽  
Nakju Doh
Keyword(s):  
Point Cloud ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Reconstruction Method ◽  
Complex Environments ◽  
Indoor Space ◽  
Multi Level ◽  
Level Building ◽  
Indoor Spaces ◽  
Data Term

Remarkable progress in the development of modeling methods for indoor spaces has been made in recent years with a focus on the reconstruction of complex environments, such as multi-room and multi-level buildings. Existing methods represent indoor structure models as a combination of several sub-spaces, which are constructed by room segmentation or horizontal slicing approach that divide the multi-room or multi-level building environments into several segments. In this study, we propose an automatic reconstruction method of multi-level indoor spaces with unique models, including inter-room and inter-floor connections from point cloud and trajectory. We construct structural points from registered point cloud and extract piece-wise planar segments from the structural points. Then, a three-dimensional space decomposition is conducted and water-tight meshes are generated with energy minimization using graph cut algorithm. The data term of the energy function is expressed as a difference in visibility between each decomposed space and trajectory. The proposed method allows modeling of indoor spaces in complex environments, such as multi-room, room-less, and multi-level buildings. The performance of the proposed approach is evaluated for seven indoor space datasets.

A MPTCP Path Selection Strategy Based on Improved Grey Relational Analysis

2013 ◽  
Vol 401-403 ◽  
pp. 1766-1771 ◽  
Author(s):  
Lan Kou ◽  
Si Rui Chen ◽  
Rui Wang
Keyword(s):  
Transmission Control Protocol ◽  
Path Selection ◽  
Transport Layer ◽  
Reference Sequence ◽  
Gray Relational Analysis ◽  
Selection Strategy ◽  
Relational Analysis ◽  
Multiple Paths ◽  
End To End ◽  
Grey Relational

Multipath Transmission Control Protocol (MPTCP), a transport layer protocol, proposed by the IETF working group in 2009, can provide multipath communication end to end. It also can improve the utilization of network resources and network transmission reliability. However, that how to select multiple paths to improve the end to end overall throughput, and how to avoid the throughput declining by the performance difference, become the focus of this study. We propose a path selection strategy based on improved gray relational analysis, and set the optimal values of the QoS parameters for the selected paths as the reference sequence. According to the value of improved grey relational degree (IGRD) which is compared with reference sequence, we select the paths with better performance, smaller difference for transmission.

scholarly journals A New Approach to Measuring the Similarity of Indoor Semantic Trajectories

2021 ◽  
Vol 10 (2) ◽  
pp. 90
Author(s):  
Jin Zhu ◽  
Dayu Cheng ◽  
Weiwei Zhang ◽  
Ci Song ◽  
Jie Chen ◽  
...  
Keyword(s):  
Similarity Measure ◽  
Semantic Information ◽  
Edit Distance ◽  
Similarity Measures ◽  
Indoor Positioning ◽  
Synthetic Dataset ◽  
Shopping Mall ◽  
Indoor Space ◽  
Trajectory Similarity ◽  
Indoor Spaces

People spend more than 80% of their time in indoor spaces, such as shopping malls and office buildings. Indoor trajectories collected by indoor positioning devices, such as WiFi and Bluetooth devices, can reflect human movement behaviors in indoor spaces. Insightful indoor movement patterns can be discovered from indoor trajectories using various clustering methods. These methods are based on a measure that reflects the degree of similarity between indoor trajectories. Researchers have proposed many trajectory similarity measures. However, existing trajectory similarity measures ignore the indoor movement constraints imposed by the indoor space and the characteristics of indoor positioning sensors, which leads to an inaccurate measure of indoor trajectory similarity. Additionally, most of these works focus on the spatial and temporal dimensions of trajectories and pay less attention to indoor semantic information. Integrating indoor semantic information such as the indoor point of interest into the indoor trajectory similarity measurement is beneficial to discovering pedestrians having similar intentions. In this paper, we propose an accurate and reasonable indoor trajectory similarity measure called the indoor semantic trajectory similarity measure (ISTSM), which considers the features of indoor trajectories and indoor semantic information simultaneously. The ISTSM is modified from the edit distance that is a measure of the distance between string sequences. The key component of the ISTSM is an indoor navigation graph that is transformed from an indoor floor plan representing the indoor space for computing accurate indoor walking distances. The indoor walking distances and indoor semantic information are fused into the edit distance seamlessly. The ISTSM is evaluated using a synthetic dataset and real dataset for a shopping mall. The experiment with the synthetic dataset reveals that the ISTSM is more accurate and reasonable than three other popular trajectory similarities, namely the longest common subsequence (LCSS), edit distance on real sequence (EDR), and the multidimensional similarity measure (MSM). The case study of a shopping mall shows that the ISTSM effectively reveals customer movement patterns of indoor customers.

scholarly journals Human-centred Mobile Pedestrian Navigation Systems

2011 ◽  
Vol 153 ◽  
pp. 237-250 ◽  
Author(s):  
Georg Garnter ◽  
Haosheng Huang ◽  
Alexandra Millonig ◽  
Manuela Schmidt ◽  
Felix Ortag
Keyword(s):  
Navigation Systems ◽  
Pedestrian Navigation
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