High Precision 3D Indoor Routing on Reduced Visibility Graphs

Location-based service is one of the most popular buzzwords in the field of U-city. Positioning a user is an essential ingredient of a location-based system on a U-city. For the outdoor positioning, GPS based practical solutions have been introduced. However, GPS measurement error is too big to be used for U-campus services because the size of a campus is relatively smaller than a city. We propose Relative-Interpolation Method in order to improve the correctness of outdoor positioning. Besides, indoor positioning is necessary for U-campus while GPS signal is not available inside buildings. For the indoor positioning, Cricket, Active Badge, and so on have been introduced. These methods require special equipments dedicated for positioning. Our method does not require such equipments because it determines the user’s position based on the receiver signal strength indicators (RSSI) from access points (AP) which are already installed for WLAN. The algorithm we are using for indoor positioning is a kind of finger prints method. However our algorithm builds a decision tree instead of a look-up table in the off-line phase. Therefore, our method is faster than existing indoor positioning methods in the real-time phase. We have integrated our indoor and outdoor positioning methods and implemented a prototype of our indoor-outdoor positioning method on a laptop. Our experimental results are discussed in this paper.

Indoor positioning systems have already been introduced in commercial facilities. Since, the signals transmitted from GPS satellites do not penetrate inside buildings, Wifi, Zigbee and Bluetooth are used for indoor position estimation. In this work, we only focus on the use of Bluetooth due to its advantages such as low power consumption, wide signal range and inexpensive. However, the accuracy of positioning is not sufficient in current technologies. Therefore, this paper proposes an indoor positioning method for improving accuracy, using compass and walking speed with an extended Kalman filter. Preliminary experimental results improves accuracy up to 21.2%.

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.

LED visible light communication indoor positioning method based on the mobile phone at any horizontal orientation angle. The system includes a single LED downlight, a mobile phone orientation sensor, a mobile phone front single camera and a positioning system. The steps include: the orientation sensor of the handset is used to measure the orientation of the handset in the horizontal direction; the developed camera is used to call the front camera of the handset to record the LED downlight image; and the image is processed and decoded to obtain the ID information of the LED downlight, which is the three-dimensional position coordinates information of the downlight in the indoor space. Take the previously obtained information including the handset horizontal orientation and downlight ID information into the developed indoor positioning method to obtain the real-time accurate position of the mobile phone. The positioning method is simple and easy to operate. The mobile phone can be accurately positioned in real time at any horizontal orientation angle just by using the front camera of mobile phone in the case of just a single LED downlight. This method overcomes the problem that even if the position of the camera is kept unchanged, the positioning result of the mobile phone is different due to the difference of the horizontal orientation of the mobile phone. This method is more feasible and accurate.

Autonomous unmanned aerial vehicles (UAVs) need to dynamically re-plan paths online to avoid newly detected obstacles and no-fly zones. Existing 3D path planning methods are either too computationally intensive for online use or have practical limitations for actual applications. We propose a new method based on visibility graphs that is both computationally efficient for online use and is suitable for actual applications. We consider the 3D space to be composed of many 2D planes that all pass through the current position of the UAV and the destination point. Finding the shortest collision-free path in each plane is a 2D path planning problem which can be solved by using existing visibility graph algorithms. We then collect all the shortest paths generated from each 2D plane and find the shortest path in the whole 3D space. We present the results of the proposed 3D path planning algorithm for two cases to demonstrate that the proposed method is effective.

Children with severe physical disabilities are denied their fundamental right to move, restricting their development, independence, and participation in life. Brain-computer interfaces (BCIs) could enable children with complex physical needs to access power mobility (PM) devices, which could help them move safely and independently. BCIs have been studied for PM control for adults but remain unexamined in children. In this study, we explored the feasibility of BCI-enabled PM control for children with severe physical disabilities, assessing BCI performance, standard PM skills and tolerability of BCI. Patient-oriented pilot trial. Eight children with quadriplegic cerebral palsy attended two sessions where they used a simple, commercial-grade BCI system to activate a PM trainer device. Performance was assessed through controlled activation trials (holding the PM device still or activating it upon verbal and visual cueing), and basic PM skills (driving time, number of activations, stopping) were assessed through distance trials. Setup and calibration times, headset tolerability, workload, and patient/caregiver experience were also evaluated. All participants completed the study with favorable tolerability and no serious adverse events or technological challenges. Average control accuracy was 78.3 ± 12.1%, participants were more reliably able to activate (95.7 ± 11.3%) the device than hold still (62.1 ± 23.7%). Positive trends were observed between performance and prior BCI experience and age. Participants were able to drive the PM device continuously an average of 1.5 meters for 3.0 s. They were able to stop at a target 53.1 ± 23.3% of the time, with significant variability. Participants tolerated the headset well, experienced mild-to-moderate workload and setup/calibration times were found to be practical. Participants were proud of their performance and both participants and families were eager to participate in future power mobility sessions. BCI-enabled PM access appears feasible in disabled children based on evaluations of performance, tolerability, workload, and setup/calibration. Performance was comparable to existing pediatric BCI literature and surpasses established cut-off thresholds (70%) of "effective" BCI use. Participants exhibited PM skills that would categorize them as "emerging operational learners." Continued exploration of BCI-enabled PM for children with severe physical disabilities is justified.

We introduce a new algorithm for computing Euclidean shortest paths in the plane in the presence of polygonal obstacles. In particular, for a given start points, we build a planar subdivision (ashortest path map) that supports efficient queries for shortest paths froms to any destination pointt. The worst-case time complexity of our algorithm isO(kn log2n), wheren is the number of vertices describing the polygonal obstacles, andk is a parameter we call the “illumination depth” of the obstacle space. Our algorithm usesO(n) space, avoiding the possibly quadratic space complexity of methods that rely on visibility graphs. The quantityk is frequently significantly smaller thann, especially in some of the cases in which the visibility graph has quadratic size. In particular,k is bounded above by the number of different obstacles that touch any shortest path froms.

Network science and graph theory offer attractive models to describe urban phenomena and examine cases of urban planning and design. However, none of the existing network analysis tools consider simultaneously human visual perception and locations of urban activities. In this paper we introduce POI VizNet, a new QGIS plug-in that constructs various graphs of unobstructed lines of sight. The plug-in integrates an increasing amount of available GIS-based data of point of interest (POI) and visibility into one readily accessible analytical framework. Graphs are created between two types of decision points during urban travelstreet intersections and POIs, origins, and destinations of travel, by connecting these potential observer's decision locations in an open space between buildings. Therefore, the created graphs illustrate hypothetical visual trajectories of a person looking for a particular POI in the city. The graph is termed Integrative Visibility Graph (IVG) as it incorporates both navigational and functional aspects of the city (Figure 1a ). IVG examines connectivity of the particular location, i.e., predefined POI within the street network. In addition to IVG, the current version of the toolbox offers two separate modules of analysis corresponding to two additional types of visibility: Street Network Visibility Graph (SNVG) -creates visual connections between decision points of the street network, i.e., street intersections (Figure 1b ), and Point of Interest Visibility Graph (POIVG) -creates visual connections between POI that are visible from each other in a given building arrangement (Figure 1c ). In addition, POI VizNet provides advanced options to build graphs using a predefined viewing distance and perceptual perspective. Visibility graphs are constructed and visualised as new layers in QGIS and delivered as network files suitable for further exploration, analysis, and visualisation in various network software packages.

This paper proposes a BLE used indoor positioning method, which combines the fingerprint method with particle filter, for improving the estimation accuracy. Particle filter can be implemented by creating a likelihood function. The proposed method makes fingerprint map based on RSSI from BLE beacon, and creates particle filter which estimates indoor position from observed RSSI. The experiment results show the effectiveness of the proposed method.

Abstract. The demand for indoor navigation is increasingly urgent in many applications such as safe management of underground spaces or location services in complex indoor environment, e.g. shopping centres, airports, museums, underground parking lot and hospitals. Indoor navigation is still a challenging research field, as currently applied indoor navigation algorithms commonly ignore important environmental and human factors and therefore do not provide precise navigation. Flexible and detailed networks representing the connectivity of spaces and considering indoor objects such as furniture are very important to a precise navigation. In this paper we concentrate on indoor navigation considering obstacles represented as polygons. We introduce a specific space subdivision based on a simplified floor plan to build the indoor navigation network. The experiments demonstrate that we are able to navigate around the obstacles using the proposed network. Considering to well-known path-finding approaches based on Medial Axis Transform (MAT) or Visibility Graph (VG), the approach in this paper provides a quick subdivision of space and routes, which are compatible with the results of VG.

We propose a simple and effective heuristic that allows fast generation of a large set of shortest path alternatives in weighted directed graphs. The heuristic is based on existing deviation path algorithms for exact k shortest paths. It precalculates a backward shortest path tree and thus avoids doing many shortest path computations, but as a result it does not necessarily find the exact set of k shortest paths. Computational results on real-world road networks are reported. Our tests show that the quality of the paths produced by the heuristic is most satisfactory: typically, the kth path found by the heuristic is less than 1% longer than the exact kth shortest path, for values of k up to 10,000. Moreover, the heuristic runs very fast. We also show how the heuristic can be enhanced to an exact k shortest paths algorithm, which performs well in comparison with the existing exact k shortest path algorithms.

Computing the shortest path between two given locations in a road network is an important problem that finds applications in various map services and commercial navigation products. The state-of-the-art solutions for the problem can be divided into two categories: spatial-coherence-based methods and vertex-importance-based approaches. The two categories of techniques, however, have not been compared systematically under the same experimental framework, as they were developed from two independent lines of research that do not refer to each other. This renders it difficult for a practitioner to decide which technique should be adopted for a specific application. Furthermore, the experimental evaluation of the existing techniques, as presented in previous work, falls short in several aspects. Some methods were tested only on small road networks with up to one hundred thousand vertices; some approaches were evaluated using distance queries (instead of shortest path queries), namely, queries that ask only for the length of the shortest path; a state-of-the-art technique was examined based on a faulty implementation that led to incorrect query results. To address the above issues, this paper presents a comprehensive comparison of the most advanced spatial-coherence-based and vertex-importance-based approaches. Using a variety of real road networks with up to twenty million vertices, we evaluated each technique in terms of its preprocessing time, space consumption, and query efficiency (for both shortest path and distance queries). Our experimental results reveal the characteristics of different techniques, based on which we provide guidelines on selecting appropriate methods for various scenarios.

In Euclidean and/or λ-geometry planes with obstacles, the shortest path problem involves determining the shortest path between a source and a destination. There are three different approaches to solve this problem in the Euclidean plane: roadmaps, cell decomposition, and potential field. In the roadmaps approach, a visibility graph is considered to be one of the most widely used methods. In this paper, we present a novel method based on the concepts of Delaunay triangulation, an improved Dijkstra algorithm and the Fermat points to construct a reduced visibility graph that can obtain the near-shortest path in a very short amount of computational time. The length of path obtained using our algorithm is the shortest in comparison to the other fastest algorithms with O(n log n) time complexity. The proposed fast algorithm is especially suitable for those applications which require determining the shortest connectivity between points in the Euclidean plane, such as the robot arm path planning and motion planning for a vehicle.

Path planning is one of the vital tasks in the intelligent control of autonomous robots. It is of prime importance from industrial as well as commercial point of view. Various autonomous vehicles are gaining popularity to reduce the on-field intervention of humans. The path planning problem is divided into two sub-problems: finding the feasible node pairs and then evaluate the shortest feasible path from the obtained feasible node pairs. Feasible node pair is the part of the path which do not pass through any obstacle. For the known static arena, the most popular method to solve these kinds of problems is the visibility graph (VGraph), but it is computationally intensive. This paper proposes an Enhanced Linear Graph (ELGraph), a method for finding feasible node pairs. The ELGraph has simplified linear structure which exploits the concept of line segment intersection, which may take low computational time. The simulations were carried out for VGraph and ELGraph to get the feasible node pair, and then Dijkstra algorithm evaluates the shortest feasible path. The results obtained shows that ELGraph takes lesser evaluation time and is computationally lesser intensive than VGraph, without compromising on finding the shortest possible feasible path.

Graph-based approaches for simulating pedestrian dynamics in building models