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 and consider future directions. 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?

In this paper, we consider the computation of the D-optimality criterion as a metric for the uncertainty of a SLAM system. Properties regarding the use of this uncertainty criterion in the active SLAM context are highlighted, and comparisons against the A-optimality criterion and entropy are presented. This paper shows that contrary to what has been previously reported, the D-optimality criterion is indeed capable of giving fruitful information as a metric for the uncertainty of a robot performing SLAM. Finally, through various experiments with simulated and real robots, we support our claims and show that the use of D-opt has desirable effects in various SLAM related tasks such as active mapping and exploration.

In this paper we examine the problem of autonomously exploring and mapping an environment using a mobile robot. The robot uses a graph-based SLAM system to perform mapping and represents the map as an occupancy grid. In this setting, the robot must trade-off between exploring new area to complete the task and exploiting the existing information to maintain good localization. Selecting actions that decrease the map uncertainty while not significantly increasing the robot's localization uncertainty is challenging. We present a novel information-theoretic utility function that uses both Shannon's and Rényi's definitions of entropy to jointly consider the uncertainty of the robot and the map. This allows us to fuse both uncertainties without the use of manual tuning. We present simulations and experiments comparing the proposed utility function to state-of-the-art utility functions, which only use Shannon's entropy. We show that by using the proposed utility function, the robot and map uncertainties are smaller than using other existing methods.

This paper presents the design and implementation of an Internet of Thing (IoT)-based system for indoor localization using Bluetooth Low Energy (BLE) technology. Our solution consists of two main systems: an acquisition system and a central server, under the Client-Server paradigm and the IoT philosophy. We report the development of different modules: measurement (Bluetooth beacons), data aggregation and transmission, storage, web-interface and cloud services for data, and results visualization. The localization mechanism of the proposed system is based on a simple location algorithm stemming from the Received Signal Strength (RSS) footprinting method, which allow us to detect reference zones inside closed environments. We performed real experiments in order to assess the proposed system. This paper reports the design, implementation and evaluation of an IoT-based system for indoor location using Bluetooth Low Energy (BLE) technology.

In this paper we investigate the monotonicity of various optimality criteria during the exploration phase of an active SLAM algorithm. Optimality criteria such as A-opt, D-opt or E-opt are used in active SLAM to account for uncertainty in the map or the robot's pose, and these criteria are usually part of utility functions which help active SLAM algorithms decide where the robot should move next. The monotonicity of the optimality criteria is of utmost importance. During the exploration phase, i.e. when the robot is traversing new territory or cannot perform a loop closure, the most common way of estimating the pose of the robot is through dead-reckoning. Correctly accounting for the uncertainty is important for an active SLAM algorithm and in particular for a dead-reckoning scenario, where by definition the uncertainty in the robot's pose grows. If monotonicity does not hold in this scenario, active SLAM algorithms can execute actions under the false belief that the uncertainty has reduced. We show analytically and experimentally some conditions in which the A-opt and E-opt criteria lose monotonicity in a dead-reckoning scenario, where the propagation of the robot's pose is done using a linearized framework. We also show analytically and experimentally that under the same conditions the D-opt does not lose monotonicity and, in general for the linearized framework under consideration, D-opt does not break monotonicity.