Navigation In Indoor Environments Research Articles

A WiFi-Based Weighted Screening Method for Indoor Positioning Systems

Past studies of WiFi-based indoor positioning systems (IPS) have mainly been divided into three types of positioning: proximity, trilateration, and scene analysis. This paper proposes a WiFi-based weighted screening method (WSM) to improve displacement error in trilateration. WSM is designed for part of pedestrian navigation in indoor environment which most likely use smart phone as the WiFi signal detector. Thus the less computational power consuming, irregular deploying position of APs and the irregular RSSI variation are all take into consideration when designing WSM. Experiment results show that this method performs better than conventional matrix method and the error correction algorithm, one trilateration in IPS.

Evaluating suitability of the least risk path algorithm to support cognitive wayfinding in indoor spaces: An empirical study

Over the last couple of years, applications that support wayfinding in indoor spaces have become a booming industry. Finding one's way in complex 3D indoor environments can still be a challenging endeavor, partly induced by the specific indoor structure (e.g. fragmentation, less visibility, confined areas). Appropriate algorithms that help guide unfamiliar users by providing ‘easier to follow’ route instructions are so far mostly absent indoors. In outdoor space, several alternative algorithms exist, adding a more cognitive notion to the calculated paths and as such adhering to the natural wayfinding behavior (e.g. simplest paths, least risk paths). The aim of this research is to extend those richer cognitive algorithms to three-dimensional indoor environments. More specifically, the focal point of this paper is the application of the least risk path algorithm, i.e. an algorithm developed to minimize the risk of getting lost, to an indoor space. This algorithm is duplicated and extensively tested in a complex multi-story building by comparing the quality of the calculated least risk paths with their shortest path alternatives. The outcome of those tests reveals non-stable results in terms of selecting the least risky edges in indoor environments, which leads to the conclusion that the algorithm has to be adjusted to the specificities of indoor space. Several improvements for the algorithm are proposed and will be implemented as part of future work to improve the overall user experience during navigation in indoor environments.

Indoor SLAM Using Laser and Camera with Closed-Loop Controller for NAO Humanoid Robot

We present a SLAM with closed-loop controller method for navigation of NAO humanoid robot from Aldebaran. The method is based on the integration of laser and vision system. The camera is used to recognize the landmarks whereas the laser provides the information for simultaneous localization and mapping (SLAM ). K-means clustering method is implemented to extract data from different objects. In addition, the robot avoids the obstacles by the avoidance function. The closed-loop controller reduces the error between the real position and estimated position. Finally, simulation and experiments show that the proposed method is efficient and reliable for navigation in indoor environments.

Analyzing the applicability of the least risk path algorithm in indoor space

Abstract. Over the last couple of years, applications that support navigation and wayfinding in indoor environments have become one of the booming industries. However, the algorithmic support for indoor navigation has so far been left mostly untouched, as most applications mainly rely on adapting Dijkstra's shortest path algorithm to an indoor network. In outdoor space, several alternative algorithms have been proposed adding a more cognitive notion to the calculated paths and as such adhering to the natural wayfinding behavior (e.g. simplest paths, least risk paths). The need for indoor cognitive algorithms is highlighted by a more challenged navigation and orientation due to the specific indoor structure (e.g. fragmentation, less visibility, confined areas…). Therefore, the aim of this research is to extend those richer cognitive algorithms to three-dimensional indoor environments. More specifically for this paper, we will focus on the application of the least risk path algorithm of Grum (2005) to an indoor space. The algorithm as proposed by Grum (2005) is duplicated and tested in a complex multi-story building. Several analyses compare shortest and least risk paths in indoor and in outdoor space. The results of these analyses indicate that the current outdoor least risk path algorithm does not calculate less risky paths compared to its shortest paths. In some cases, worse routes have been suggested. Adjustments to the original algorithm are proposed to be more aligned to the specific structure of indoor environments. In a later stage, other cognitive algorithms will be implemented and tested in both an indoor and combined indoor-outdoor setting, in an effort to improve the overall user experience during navigation in indoor environments.

A VISION-BASED LOCATION POSITIONING SYSTEM VIA AUGMENTED REALITY: AN APPLICATION IN HUMANOID ROBOT NAVIGATION

In this paper, we present a vision-based localization system using mobile augmented reality (MAR) and mobile audio augmented reality (MAAR) techniques, applicable to both humans and humanoid robots navigation in indoor environments. In the first stage, we propose a system that recognizes the location of a user from the image sequence of an indoor environment using its onboard camera. The location information is added to the user's view in the form of 3D objects and audio sounds with location information and navigation instruction content via augmented reality (AR). The location is recognized by using the prior knowledge about the layout of the environment and the location of the AR markers. The image sequence can be obtained using a smart phone's camera and the marker detection, 3D object placement and audio augmentation will be performed by the phone's operating processor and graphical/audio modules. Using this system will majorly reduce the hardware complexity of such navigation systems, as it replaces a system consisting of a mobile PC, wireless camera, head-mounted displays (HMD) and a remote PC with a smart phone with camera. In the second stage, the same algorithm is employed as a novel vision-based autonomous humanoid robot localization and navigation approach. The proposed technique is implemented on a humanoid robot NAO and improves the robot's navigation and localization performance previously done using an extended Kalman filter (EKF) by presenting location-based information to the robot through different AR markers placed in the robot environment.

CALCULATING LEAST RISK PATHS IN 3D INDOOR SPACE

Abstract. Over the last couple of years, research on indoor environments has gained a fresh impetus; more specifically applications that support navigation and wayfinding have become one of the booming industries. Indoor navigation research currently covers the technological aspect of indoor positioning and the modelling of indoor space. The algorithmic development to support navigation has so far been left mostly untouched, as most applications mainly rely on adapting Dijkstra's shortest path algorithm to an indoor network. However, alternative algorithms for outdoor navigation have been proposed adding a more cognitive notion to the calculated paths and as such adhering to the natural wayfinding behaviour (e.g. simplest paths, least risk paths). These algorithms are currently restricted to outdoor applications. The need for indoor cognitive algorithms is highlighted by a more challenged navigation and orientation due to the specific indoor structure (e.g. fragmentation, less visibility, confined areas…). As such, the clarity and easiness of route instructions is of paramount importance when distributing indoor routes. A shortest or fastest path indoors not necessarily aligns with the cognitive mapping of the building. Therefore, the aim of this research is to extend those richer cognitive algorithms to three-dimensional indoor environments. More specifically for this paper, we will focus on the application of the least risk path algorithm of Grum (2005) to an indoor space. The algorithm as proposed by Grum (2005) is duplicated and tested in a complex multi-storey building. The results of several least risk path calculations are compared to the shortest paths in indoor environments in terms of total length, improvement in route description complexity and number of turns. Several scenarios are tested in this comparison: paths covering a single floor, paths crossing several building wings and/or floors. Adjustments to the algorithm are proposed to be more aligned to the specific structure of indoor environments (e.g. no turn restrictions, restricted usage of rooms, vertical movement) and common wayfinding strategies indoors. In a later stage, other cognitive algorithms will be implemented and tested in both an indoor and combined indoor-outdoor setting, in an effort to improve the overall user experience during navigation in indoor environments.

HCTNav: A Path Planning Algorithm for Low-Cost Autonomous Robot Navigation in Indoor Environments

Low-cost robots are characterized by low computational resources and limited energy supply. Path planning algorithms aim to find the optimal path between two points so the robot consumes as little energy as possible. However, these algorithms were not developed considering computational limitations (i.e., processing and memory capacity). This paper presents the HCTNav path-planning algorithm (HCTLab research group’s navigation algorithm). This algorithm was designed to be run in low-cost robots for indoor navigation. The results of the comparison between HCTNav and the Dijkstra’s algorithms show that HCTNav’s memory peak is nine times lower than Dijkstra’s in maps with more than 150,000 cells.

Fusion of Building Information and Range Imaging for Autonomous Location Estimation in Indoor Environments

We present a novel approach for autonomous location estimation and navigation in indoor environments using range images and prior scene knowledge from a GIS database (CityGML). What makes this task challenging is the arbitrary relative spatial relation between GIS and Time-of-Flight (ToF) range camera further complicated by a markerless configuration. We propose to estimate the camera's pose solely based on matching of GIS objects and their detected location in image sequences. We develop a coarse-to-fine matching strategy that is able to match point clouds without any initial parameters. Experiments with a state-of-the-art ToF point cloud show that our proposed method delivers an absolute camera position with decimeter accuracy, which is sufficient for many real-world applications (e.g., collision avoidance).

Acquisition of Behavioral Dynamics for Vision Based Mobile Robot Navigation from Demonstrations

The design of robust vision based robot navigation behaviors remains a challenge in mobile robotics as it requires a coherent mapping between a complex visual perception and its associated robot motion. This contribution proposes a framework to learn this general relationship from a small set of representative demonstrations in which an expert manually navigates the robot through its environment. Behaviors are represented by a dynamic system that ties the perceptions to actions. The state of the behavioral dynamics is characterized by a small set of visual features extracted from an omnidirectional image of the local environment. Recording, learning and generalization takes place in the product space of visual features and robot controls. Training instances are recorded for three distinctive behaviors namely corridor following, obstacle avoidance and homing. Behavioral dynamics are represented as Gaussian mixture models, parameters of which are identified from the recorded demonstrations. The learned behaviors are able to accomplish the task across a diverse set of initial poses and situations. In order to realize global navigation, the behaviors are coordinated via hand designed arbitration or command fusion schemes. The experimental validation of the proposed approach confirms that the acquired visual navigation behaviors in cooperation accomplish robust navigation in indoor environments.

An Indoor Navigation System for the Visually Impaired

Navigation in indoor environments is highly challenging for the severely visually impaired, particularly in spaces visited for the first time. Several solutions have been proposed to deal with this challenge. Although some of them have shown to be useful in real scenarios, they involve an important deployment effort or use artifacts that are not natural for blind users. This paper presents an indoor navigation system that was designed taking into consideration usability as the quality requirement to be maximized. This solution enables one to identify the position of a person and calculates the velocity and direction of his movements. Using this information, the system determines the user's trajectory, locates possible obstacles in that route, and offers navigation information to the user. The solution has been evaluated using two experimental scenarios. Although the results are still not enough to provide strong conclusions, they indicate that the system is suitable to guide visually impaired people through an unknown built environment.

Ultra wideband indoor navigation system

Typical indoor environments contain multiple walls and obstacles consisting of different materials. As a result, current narrowband radio frequency (RF) indoor navigation systems cannot satisfy the challenging demands for most indoor applications. The RF ultra wideband (UWB) system is a promising technology for indoor localisation owing to its high bandwidth that permits mitigation of the multipath identification problem. This work proposes a novel UWB navigation system that permits accurate mobile robot (MR) navigation in indoor environments. The navigation system is composed of two sub-systems: the localisation system and the MR control system. The main contributions of this work are focused on estimation algorithm for localisation, digital implementation of transmitter and receiver and integration of both sub-systems that enable autonomous robot navigation. For sub-systems performance evaluation, statics and dynamics experiments were carried out which demonstrated that the proposed system reached an accuracy that outperforms traditional sensors technologies used in robot navigation, such as odometer and sonar.

Fast Vertical Line Correspondence Between Images for Mobile Robot Localization

Abstract The localization of a mobile robot can be estimated by using a single camera attached to it, and by previously knowing the position of a few visual landmarks. This approach is based on the tracking of features over image sequences acquired during robot navigation in indoor environments which, typically, have many vertical lines. In this article, we propose a new and fast method that extracts projections of vertical lines and matches them between consecutive frames. This method is based on a new metric, which considers color distributions in the neighborhoods of the line projections, and on the use of search windows defined by robot displacements. In this way, we obtain an effective and efficient matching process, with empirical evidence that showed promising results.

Using Low-Power Radios for Mobile Robots Navigation

Abstract This paper addresses the general field of mobile robots navigation within fixed sensor networks. We focus on an innovative navigation for indoor environments that is based on Radio-Frequency (RF) communication and its Received Signal Strength (RSS). We propose two methods for guiding a robot towards an RF beacon using low-power radios, namely the oblivious and the iterative methods. While the former has negligeable resource requirements, the latter requires more memory, processing power and odometry but it presents a substantially improved performance. We noticed that noisy RSS measurements are the main obstacle in our problem, since the noise incurs great ambiguity in the RSS gradient climbing. Thus, we introduce antenna diversity in the beacon and omnidirectional antenna with shielding plane in the robot to reduce multi-path phenomena and provide a better relationship between robot-beacon distance and RSS. The efficiency of these techniques as well as the performance of the oblivious and iterative methods are evaluated via simulations and practical experiments with an autonomous mobile robot. The results validate the methods and confirm the viability of RSS-based navigation.

Location estimation for indoor autonomous vehicle navigation by omni-directional vision using circular landmarks on ceilings

A novel approach to location estimation by omni-directional vision for autonomous vehicle navigation in indoor environments using circular landmark information is proposed. A circular-shaped landmark is attached on a ceiling and an omni-directional camera is equipped on a vehicle to take upward-looking omni-directional images of the landmark. This way of image taking reduces possible landmark shape occlusion and image noise creation, which come from the existence of nearby objects or humans surrounding the vehicle. It is shown that the perspective shape of the circular landmark in the omni-directional image may be approximated by an ellipse by analytic formulas with good shape-fitting effect and fast computation speed. The parameters of the ellipse are then used for estimating the location of the vehicle with good precision for navigation guidance. Both simulated and real images were tested and good experimental results confirm the feasibility of the proposed approach.

Autonomous image-based navigation in indoor environment

In this paper, an image-based framework for navigation of a mobile robot in an indoor environment is presented. The only sensor used is an embedded monocular vision system. The environment is autonomously mapped during a learning stage in order to locate the robot on-line. A suitable control law is designed to drive the robot in an image database. To address this issue, a Virtual Non Holonomic Vehicle (VNHV) attached to the image plane is defined.

Behavior-based neuro-fuzzy controller for mobile robot navigation

This paper discusses a neuro-fuzzy controller for sensor-based mobile robot navigation in indoor environments. The control system consists of a hierarchy of robot behaviors.

A Navigation System for Cleaning Robots

Free navigation in indoor environments is one of the main enabling technologies for many service robot applications. The SIEMENS navigation system SINAS which is primarily targeted towards cleaning robot applications, has proved its suitability for tough everyday operation since August 1996 on several occasions, e.g., in several chain store supermarkets. This paper discusses the main requirements of a navigation system for cleaning robots, presents the architecture and main modules of the SINAS system, and reports on real-world experiences.

SINAS – a navigation system for service robots

Free navigation in indoor environments is one of the main enabling technologies for many service robot applications. Siemens has developed SINAS, a navigation system which currently is primarily targeted towards cleaning robot applications. Its suitability for tough everyday operation has been successfully demonstrated since August 1996 on several occasions, e.g. in several chain store supermarkets. The paper discusses the main requirements of a navigation system for cleaning robots, presents the structure and main features of the SINAS system, and reports experiences and results from the field tests.

Obstacle avoidance for autonomous land vehicle navigation in indoor environments by quadratic classifier

A vision-based approach to obstacle avoidance for autonomous land vehicle (ALV) navigation in indoor environments is proposed. The approach is based on the use of a pattern recognition scheme, the quadratic classifier, to find collision-free paths in unknown indoor corridor environments. Obstacles treated in this study include the walls of the corridor and the objects that appear in the way of ALV navigation in the corridor. Detected obstacles as well as the two sides of the ALV body are considered as patterns. A systematic method for separating these patterns into two classes is proposed. The two pattern classes are used as the input data to design a quadratic classifier. Finally, the two-dimensional decision boundary of the classifier, which goes through the middle point between the two front vehicle wheels, is taken as a local collision-free path. This approach is implemented on a real ALV and successful navigations confirm the feasibility of the approach.

Smooth vision-based autonomous land vehicle navigation in indoor environments by person following using sequential pattern recognition

A new approach to autonomous land vehicle ALV navigation by the person follow- ing is proposed. This approach is based on sequential pattern recognition and com- puter vision techniques, and maintenance of smoothness for indoor navigation is the main goal. The ALV is guided automatically to follow a person who walks in front of the vehicle. The vehicle can be used as an autonomous handcart, go-cart, buffet car, golf cart, weeder, etc. in various applications. Sequential pattern recognition is used to design a classifier for making decisions about whether the person in front of the vehicle is walking straight or is too right or too left of the vehicle. Multiple images in a sequence are used as input to the system. Computer vision techniques are used to detect and locate the person in front of the vehicle. By sequential pattern recognition, the relation between the location of the person and that of the vehicle is categorized into three classes. Corresponding adjustments of the direction of the vehicle are computed to achieve smooth navigation. The approach is implemented on a real ALV, and successful and smooth navigation sessions confirm the feasibility of the approach.