Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

Real-time rss-based indoor navigation for autonomous UAV flight

Navigation for the autonomous flight of Unmanned Aerial Vehicles (UAVs) in an indoor space has attracted much attention recently. One of the main goals of an indoor navigation system is developing an alternative method to obtain position information that can replace or complement the global positioning system. While much research has focused on vision-based indoor navigation systems, this paper aims to develop a Received Signal Strength (RSS)-based navigation system, which is a more cost effective alternative. Then, the position and attitude of a UAV can be computed by the fusion of RSS measurements and measurements from the onboard inertial measurement unit. In order to improve the estimation accuracy, we first consider a mathematical model of the RSS-based navigation system and formulate optimization problems to compute the parameter values which minimize the RSS measurement error. Using the optimal parameters, an autonomous flight system is developed whose estimator and controller components are designed to work well with the RSS-based navigation system. Simulations and experiments using a quadrotor demonstrate the feasibility and performance of the proposed RSS-based navigation system for UAVs operating in indoor environments

Improved Localization Algorithms in Indoor Wireless Environment

Localization has been considered as an important precondition for the location-dependent applications such as mobile tracking and navigation.To obtain specific location information, we usually make use of Global Positioning System(GPS), which is the most common plat- form to acquire localization information in outdoor environments. When targets are in indoor environment, however, the GPS signal is usually blocked, so we also consider other assisted positioning techniques in order to obtain accurate position of targets. In this thesis, three different schemes in indoor environment are proposed to minimize localization error by placing refer- ence nodes in optimum locations, combining the localization information from accelerometer sensor in smartphone with Received Signal Strength (RSS) from reference nodes, and utilizing frequency diversity in Wireless Fidelity (WiFi) environment. Deployments of reference nodes are vital for locating nearby targets since they are used to estimate the distances from them to the targets. A reference nodes’ placement scheme based on minimizing the average mean square error of localization over a certain region is proposed in this thesis first and is applied in different localization regions which are circular, square and hexagonal for illustration of the flexibility of the proposed scheme. Equipped with accelerometer sensor, smartphone provides useful information which out- puts accelerations in three different directions. Combining acceleration information from smart- phones and signal strength information from reference nodes to prevent the accumulated error from accelerometer is studied in this thesis. The combined locating error is narrowed by as- signing different weights to localization information from accelerometer and reference nodes. In indoor environment, RSS technology based localization is the most common way to imply since it require less additional hardware compared to other localization technologies. However, RSS can be affected greatly by complex circumstance as well as carrier frequency. Utilization of diverse frequencies to improve localization performance is proposed in the end of this thesis along with some experiments applied on Software Defined Platform (SDR)

Location Estimation in Wireless Communication Systems

Localization has become a key enabling technology in many emerging wireless applications and services. One of the most challenging problems in wireless localization technologies is that the performance is easily affected by the signal propagation environment. When the direct path between transmitter and receiver is obstructed, the signal measurement error for the localization process will increase significantly. Considering this problem, we first propose a novel algorithm which can automatically detect and remove the obstruction and improve the localization performance in complex environment. Besides the environmental dependency, the accuracy of target location estimation is highly sensitive to the positions of reference nodes. In this thesis, we also study on the reference node placement, and derive an optimum deployment scheme which can provide the best localization accuracy. Another challenge of wireless localization is due to insufficient number of reference nodes available in the target\u27s communication range. In this circumstance, we finally utilize the internal sensors in today\u27s smartphones to provide additional information for localization purpose, and propose a novel algorithm which can combine the location dependent parameters measured from sensors and available reference nodes together. The combined localization algorithm can overcome the error accumulation from sensor with the help of only few number of reference nodes

Localisation in wireless sensor networks for disaster recovery and rescuing in built environments

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyProgress in micro-electromechanical systems (MEMS) and radio frequency (RF) technology has fostered the development of wireless sensor networks (WSNs). Different from traditional networks, WSNs are data-centric, self-configuring and self-healing. Although WSNs have been successfully applied in built environments (e.g. security and services in smart homes), their applications and benefits have not been fully explored in areas such as disaster recovery and rescuing. There are issues related to self-localisation as well as practical constraints to be taken into account. The current state-of-the art communication technologies used in disaster scenarios are challenged by various limitations (e.g. the uncertainty of RSS). Localisation in WSNs (location sensing) is a challenging problem, especially in disaster environments and there is a need for technological developments in order to cater to disaster conditions. This research seeks to design and develop novel localisation algorithms using WSNs to overcome the limitations in existing techniques. A novel probabilistic fuzzy logic based range-free localisation algorithm (PFRL) is devised to solve localisation problems for WSNs. Simulation results show that the proposed algorithm performs better than other range free localisation algorithms (namely DVhop localisation, Centroid localisation and Amorphous localisation) in terms of localisation accuracy by 15-30% with various numbers of anchors and degrees of radio propagation irregularity. In disaster scenarios, for example, if WSNs are applied to sense fire hazards in building, wireless sensor nodes will be equipped on different floors. To this end, PFRL has been extended to solve sensor localisation problems in 3D space. Computational results show that the 3D localisation algorithm provides better localisation accuracy when varying the system parameters with different communication/deployment models. PFRL is further developed by applying dynamic distance measurement updates among the moving sensors in a disaster environment. Simulation results indicate that the new method scales very well