Recently there has been an increased interest in the development of autonomous flying vehicles. Whereas most of the proposed approaches are suitable for outdoor operation, only a few techniques have been designed for indoor environments, where the systems cannot rely on GPS and therefore have to use their exteroceptive sensors for navigation. In this paper we present a general navigation system which enables a small-sized quadrotor system to autonomously operate in indoor environments. To achieve this, we systematically extend and adapt techniques which have been successfully applied on ground robots. We describe all algorithms and present a broad set of experiments illustrating that they enable a quadrotor robot to reliably and autonomously navigate in indoor environments.

Abstract — This paper describes a new algorithm for cooperative and persistent simultaneous localization and mapping (SLAM) using multiple robots. Recent pose graph representations have proven very successful for single robot mapping and localization. Among these methods, incremental smoothing and mapping (iSAM) gives an exact incremental solution to the SLAM problem by solving a full nonlinear optimization problem in real-time. In this paper, we present a novel extension to iSAM to facilitate online multi-robot mapping based on multiple pose graphs. Our main contribution is a relative formulation of the relationship between multiple pose graphs that avoids the initialization problem and leads to an efficient solution when compared to a completely global formulation. The relative pose graphs are optimized together to provide a globally consistent multi-robot solution. Efficient access to covariances at any time for relative parameters is provided through iSAM, facilitating data association and loop closing. The performance of the technique is illustrated on various data sets including a publicly available multi-robot data set. Further evaluation is performed in a collaborative helicopter and ground robot experiment. I.

Being able to build a map of the environment and to simultaneously localize within this map is an essential skill for mobile robots navigating in unknown environments in absence of external referencing systems such as GPS. This so-called simultaneous localization and mapping (SLAM) problem has been one of the most popular research topics in mobile robotics for the last two decades and efficient approaches for solving this task have been proposed. One intuitive way of formulating SLAM is to use a graph whose nodes correspond to the poses of the robot at different points in time and whose edges represent constraints between the poses. The latter are obtained from observations of the environment or from movement actions carried out by the robot. Once such a graph is constructed, the map can be computed by finding the spatial configuration of the nodes that is mostly consistent with the measurements modeled by the edges. In this paper, we provide an introductory description to the graph-based SLAM problem. Furthermore, we discuss a state-of-the-art solution that is based on least-squares error minimization and exploits the structure of the SLAM problems during optimization. The goal of this tutorial is to enable the reader to implement the proposed methods from scratch.

Abstract—For many applications in Urban Search and Rescue (USAR) scenarios robots need to learn a map of unknown environments. We present a system for fast online learning of occupancy grid maps requiring low computational resources. It combines a robust scan matching approach using a LIDAR system with a 3D attitude estimation system based on inertial sensing. By using a fast approximation of map gradients and a multi-resolution grid, reliable localization and mapping capabilities in a variety of challenging environments are realized. Multiple datasets showing the applicability in an embedded handheld mapping system are provided. We show that the system is sufficiently accurate as to not require explicit loop closing techniques in the considered scenarios. The software is available as an open source package for ROS.

The central challenge in robotic mapping is obtaining reliable data associations (or “loop closures”): state-of-the-art inference algorithms can fail catastrophically if even one erroneous loop closure is incorporated into the map. Consequently, much work has been done to push error rates closer to zero. However, a long-lived or multi-robot system will still encounter errors, leading to system failure. We propose a fundamentally different approach: allow richer error models that allow the probability of a failure to be explicitly modeled. In other words, rather than characterizing loop closures as being “right ” or “wrong”, we propose characterizing the error of those loop closures in a more expressive manner that can account for their non-Gaussian behavior. Our approach leads to an fully-integrated Bayesian framework for dealing with error-prone data. Unlike earlier multiple-hypothesis approaches, our approach avoids exponential memory complexity and is fast enough for real-time performance. We show that the proposed method not only allows loop closing errors to be automatically identified, but also that in extreme cases, the “front-end ” loop-validation systems can be unnecessary. We demonstrate our system both on standard benchmarks and on the real-world datasets that motivated this work. 1

This dissertation is concerned with three-dimensional (3D) sensing and 3D scan representation. Three-dimensional records are important tools in several, quite diverse, disciplines; such as medical imaging, archaeology, and mobile robotics. In the case of mobile robotics (the discipline that is primarily targeted by the present work), 3D scanning of the environment is useful in several subtasks, such as mapping, localisation, and extraction of semantic information from the robot’s environment. This dissertation proposes the normal-distributions transform, NDT, as a general 3D surface representation with applications in scan registration, localisation, loop detection, and surface-structure analysis. Range scanners typically produce data in the form of point clouds. After applying NDT to the original discrete point samples, the scanned surface is instead represented by a piecewise smooth function with analytic first- and secondorder derivatives. Such a representation has a number of attractive properties. The smooth function representation makes it possible to use standard methods

In graph-based simultaneous localization and mapping, the pose graph grows over time as the robot gathers information about the environment. An ever growing pose graph, however, prevents long-term mapping with mobile robots. In this paper, we address the problem of efficient information-theoretic compression of pose graphs. Our approach estimates the mutual information between the laser measurements and the map to discard the measurements that are expected to provide only a small amount of information. Our method subsequently marginalizes out the nodes from the pose graph that correspond to the discarded laser measurements. To maintain a sparse pose graph that allows for efficient map optimization, our approach applies an approximate marginalization technique that is based on Chow-Liu trees. Our contributions allow the robot to effectively restrict the size of the pose graph.Alternatively, the robot is able to maintain a pose graph that does not grow unless the robot explores previously unobserved parts of the environment. Real-world experiments demonstrate that our approach to pose graph compression is well suited for long-term mobile robot mapping. 1

Maintaining a map of an environment that changes over time is a critical challenge in the development of persistently autonomous mobile robots. Many previous approaches to mapping assume a static world. In this work we incorporate the time dimension into the mapping process to enable a robot to maintain an accurate map while operating in dynamical environments. This paper presents Dynamic Pose Graph SLAM (DPG-SLAM), an algorithm designed to enable a robot to remain localized in an environment that changes substantially over time. Using incremental smoothing and mapping (iSAM) as the underlying SLAM state estimation engine, the Dynamic Pose Graph evolves over time as the robot explores new places and revisits previously mapped areas. The approach has been implemented for planar indoor environments, using laser scan matching to derive constraints for SLAM state estimation. Laser scans for the same portion of the environment at different times are compared to perform change detection; when sufficient change has occurred in a location, the dynamic pose graph is edited to remove old poses and scans that no longer match the current state of the world. Experimental results are shown for two real-world dynamic indoor laser data sets, demonstrating the ability to maintain an up-to-date map despite long-term environmental changes.

Tasks like search-and-rescue and urban reconnaissance benefit from large numbers of robots working together, but high levels of autonomy are needed in order to reduce operator requirements to practical levels. Reducing the reliance of such systems on human operators presents a number of technical challenges including automatic task allocation, global state and map estimation, robot perception, path planning, communications, and human-robot interfaces. This paper describes our 14-robot team, designed to perform urban reconnaissance missions, that won the MAGIC 2010 competition. This paper describes a variety of autonomous systems which require minimal human effort to control a large number of autonomously exploring robots. Maintaining a consistent global map, essential for autonomous planning and for giving humans situational awareness, required the development of fast loop-closing, map optimization, and communications algorithms. Key to our approach was a decoupled centralized planning architecture that allowed individual robots to execute tasks myopically, but whose behavior was coordinated centrally. In this paper, we will describe technical contributions throughout our system that played a significant role in the performance of our system. We will also present results from our system both from the competition and from subsequent quantitative evaluations, pointing out areas in which the system performed well and where interesting research problems remain.

Abstract — This paper describes a system enabling rapid multi-floor indoor map building using a body-worn sensor system fusing information from RGB-D cameras, LIDAR, inertial, and barometric sensors. Our work is motivated by rapid response missions by emergency personnel, in which the capability for one or more people to rapidly map a complex indoor environment is essential for public safety. Human-portable mapping raises a number of challenges not encountered in typical robotic mapping applications including complex 6-DOF motion and the traversal of challenging trajectories including stairs or elevators. Our system achieves robust performance in these situations by exploiting state-of-the-art techniques for robust pose graph optimization and loop closure detection. It achieves real-time performance in indoor environments of moderate scale. Experimental results are demonstrated for human-portable mapping of several floors of a university building, demonstrating the system’s ability to handle motion up and down stairs and to organize initially disconnected sets of submaps in a complex environment. address the issue where multiple users will explore regions which partially overlap. In this work, disjoint maps from a single user (as floor levels are changed and revisited) and from multiple users combine to form a set of partially overlapping maps. We use visual information to propose similar locations and to infer overlapping regions. Using these inter-map constraints, maps can be aggregated in a single combined map. In the following section, we review the background of personal localization and situational awareness systems and discuss our design prototype. Section III describes the hardware and, in Section IV, we give an overview of the algorithmic components of the mapping system. Given the challenging application we wish to support, we outline modules for estimating and correcting sensor tilt, floor traversals and user detection in Section V. I.