Structure Of Indoor Environments Research Articles

Multipath-Assisted Indoor Localization Using a Single Receiver

Target localization using a single receiver is highly needed due to the mobility of the target. In indoor environment, multipath signals are rich and usually relate the target location and structure of indoor environment through geometry parameters such as Angle of Arrival (AOA) and Time of Flight (TOF). Based on this, in this article we propose a multipath-assisted indoor localization system using commodity WiFi signals which contain phase errors caused by imperfect hardware and non-synchronized clocks. The proposed system is different from conventional localization algorithms which treat multipath signals as enemies, and only uses a single receiver. To realize accurate localization without interferences of phase errors, we firstly construct a geometry model for jointly estimating the locations of the target and scatterers which can be regarded as objects such as furniture, by using TOF differences between reflection paths and direct path. Considering more constrains on target location, we select the direct path as the reference. Then, with the help of AOAs, we develop a location searching algorithm of the target and scatterers based on Particle Swarm Optimization (PSO). We have implemented the proposed system on the commodity WiFi devices, and the experiment results in actual indoor environment show that the median location error is around 1.5m by using only one receiver. What’s more, the intensive simulation results show that the proposed system is promising in the emerging networks such as 5G, where higher bandwidth of signal and more antennas can be used for providing accurate AOA and TOF.

EXTRACTION OF THE 3D FREE SPACE FROM BUILDING MODELS FOR INDOOR NAVIGATION

Abstract. For several decades, indoor navigation has been exclusively investigated in a 2D perspective, based on floor plans, projection and other 2D representations of buildings. Nevertheless, 3D representations are closer to our reality and offer a more intuitive description of the space configuration. Thanks to recent advances in 3D modelling, 3D navigation is timidly but increasingly gaining in interest through the indoor applications. But, because the structure of indoor environment is often more complex than outdoor, very simplified models are used and obstacles are not considered for indoor navigation leading to limited possibilities in complex buildings. In this paper we consider the entire configuration of the indoor environment in 3D and introduce a method to extract from it the actual navigable space as a network of connected 3D spaces (volumes). We describe how to construct such 3D free spaces from semantically rich and furnished IFC models. The approach combines the geometric, the topological and the semantic information available in a 3D model to isolate the free space from the rest of the components. Furthermore, the extraction of such navigable spaces in building models lacking of semantic information is also considered. A data structure named combinatorial maps is used to support the operations required by the process while preserving the topological and semantic information of the input models.

Visual door detection integrating appearance and shape cues

An important component of human–robot interaction is the capability to associate semantic concepts with encountered locations and objects. This functionality is essential for visually guided navigation as well as location and object recognition. In this paper we focus on the problem of door detection using visual information only. Doors are frequently encountered in structured man-made environments and function as transitions between different places. We adopt a probabilistic approach for door detection, by defining the likelihood of various features for generated door hypotheses. Differing from previous approaches, the proposed model captures both the shape and appearance of the door. This is learned from a few training examples, exploiting additional assumptions about the structure of indoor environments. After the learning stage, we describe a hypothesis generation process and several approaches to evaluate the likelihood of the generated hypotheses. The approach is tested on numerous examples of indoor environment. It shows a good performance provided that the door extent in the images is sufficiently large and well supported by low level feature measurements.