Floor plan

Abstract: A large number of problems in AI and other areas of computer science can be viewed as special cases of the constraint-satisfaction problem. Some examples are machine vision, belief maintenance, scheduling, temporal reasoning, graph problems, floor plan design, the planning of genetic experiments, and the satisfiability problem. A number of different approaches have been developed for solving these problems. Some of them use constraint propagation to simplify the original problem. Others use backtracking to directly search for possible solutions. Some are a combination of these two techniques. This article overviews many of these approaches in a tutorial fashion.

Abstract: One potentially significant yet little investigated criterion for postoccupancy evaluation is the "legibility" of a setting—the degree to which a building facilitates the ability of users to find their way within it. The present study evaluated the legibility of a sample of ten university buildings. Self-report data indicated way-finding to be a problem for a substantial minority. The impact upon way-finding of several theoretically derived visual/spatial variables was also assessed. Two aspects of the floor plan configurations of these settings, as judged from highly significant relationships to frequency of disorientation. One of these variables, judged simplicity of floor plan configuration, was able to account for 56% of the variance in these data. One other potentially important variable, respondents' own familiarity with these buildings, was able to account for but 9% of the variance in frequency of disorientation data.

Abstract: We present Walkie-Markie - an indoor pathway mapping system that can automatically reconstruct internal pathway maps of buildings without any a-priori knowledge about the building, such as the floor plan or access point locations. Central to Walkie-Markie is a novel exploitation of the WiFi infrastructure to define landmarks (WiFi-Marks) to fuse crowdsourced user trajectories obtained from inertial sensors on users' mobile phones. WiFi-Marks are special pathway locations at which the trend of the received WiFi signal strength changes from increasing to decreasing when moving along the pathway. By embedding these WiFi-Marks in a 2D plane using a newly devised algorithm and connecting them with calibrated user trajectories, Walkie-Markie is able to infer pathway maps with high accuracy. Our experiments demonstrate that Walkie-Markie is able to reconstruct a high-quality pathway map for a real office-building floor after only 5-6 rounds of walks, with accuracy gradually improving as more user data becomes available. The maximum discrepancy between the inferred pathway map and the real one is within 3m and 2.8m for the anchor nodes and path segments, respectively.

Abstract: Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the past decades. Most radio-based solutions require a process of site survey, in which radio signatures of an interested area are annotated with their real recorded locations. Site survey involves intensive costs on manpower and time, limiting the applicable buildings of wireless localization worldwide. In this study, we investigate novel sensors integrated in modern mobile phones and leverage user motions to construct the radio map of a floor plan, which is previously obtained only by site survey. Considering user movements in a building, originally separated RSS fingerprints are geographically connected by user moving paths of locations where they are recorded, and they consequently form a high dimension fingerprint space, in which the distances among fingerprints are preserved. The fingerprint space is then automatically mapped to the floor plan in a stress-free form, which results in fingerprints labeled with physical locations. On this basis, we design LiFS, an indoor localization system based on off-the-shelf WiFi infrastructure and mobile phones. LiFS is deployed in an office building covering over 1,600 m $^2$ , and its deployment is easy and rapid since little human intervention is needed. In LiFS, the calibration of fingerprints is crowdsourced and automatic. Experiment results show that LiFS achieves comparable location accuracy to previous approaches even without site survey.

Abstract: Location determination of mobile users within a building has attracted much attention lately due to its many applications in mobile networking including network intrusion detection problems. However, it is challenging due to the complexities of the indoor radio propagation characteristics exacerbated by the mobility of the user. A common practice is to mechanically generate a table showing the radio signal strength at different known locations in the building. A mobile user's location at an arbitrary point in the building is determined by measuring the signal strength at the location in question and determining the location by referring to the above table using a LMSE (least mean square error) criterion. Obviously, this is a very tedious and time consuming task. This paper proposes a novel and automated location determination method called ARIADNE. Using a two dimensional construction floor plan and only a single actual signal strength measurement, ARIADNE generates an estimated signal strength map comparable to those generated manually by actual measurements. Given the signal measurements for a mobile, a proposed clustering algorithm searches that signal strength map to determine the current mobile's location. The results from ARIADNE are comparable and may even be superior to those from existing localization schemes.