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

Path Planning, Mapping & Learning for Mobile Robot Navigation

Ph.DDOCTOR OF PHILOSOPH

Policy-Based Planning for Robust Robot Navigation

This thesis proposes techniques for constructing and implementing an extensible navigation framework suitable for operating alongside or in place of traditional navigation systems. Robot navigation is only possible when many subsystems work in tandem such as localization and mapping, motion planning, control, and object tracking. Errors in any one of these subsystems can result in the robot failing to accomplish its task, oftentimes requiring human interventions that diminish the benefits theoretically provided by autonomous robotic systems. Our first contribution is Direction Approximation through Random Trials (DART), a method for generating human-followable navigation instructions optimized for followability instead of traditional metrics such as path length. We show how this strategy can be extended to robot navigation planning, allowing the robot to compute the sequence of control policies and switching conditions maximizing the likelihood with which the robot will reach its goal. This technique allows robots to select plans based on reliability in addition to efficiency, avoiding error-prone actions or areas of the environment. We also show how DART can be used to build compact, topological maps of its environments, offering opportunities to scale to larger environments. DART depends on the existence of a set of behaviors and switching conditions describing ways the robot can move through an environment. In the remainder of this thesis, we present methods for learning these behaviors and conditions in indoor environments. To support landmark-based navigation, we show how to train a Convolutional Neural Network (CNN) to distinguish between semantically labeled 2D occupancy grids generated from LIDAR data. By providing the robot the ability to recognize specific classes of places based on human labels, not only do we support transitioning between control laws, but also provide hooks for human-aided instruction and direction. Additionally, we suggest a subset of behaviors that provide DART with a sufficient set of actions to navigate in most indoor environments and introduce a method to learn these behaviors from teleloperated demonstrations. Our method learns a cost function suitable for integration into gradient-based control schemes. This enables the robot to execute behaviors in the absence of global knowledge. We present results demonstrating these behaviors working in several environments with varied structure, indicating that they generalize well to new environments. This work was motivated by the weaknesses and brittleness of many state-of-the-art navigation systems. Reliable navigation is the foundation of any mobile robotic system. It provides access to larger work spaces and enables a wide variety of tasks. Even though navigation systems have continued to improve, catastrophic failures can still occur (e.g. due to an incorrect loop closure) that limit their reliability. Furthermore, as work areas approach the scale of kilometers, constructing and operating on precise localization maps becomes expensive. These limitations prevent large scale deployments of robots outside of controlled settings and laboratory environments. The work presented in this thesis is intended to augment or replace traditional navigation systems to mitigate concerns about scalability and reliability by considering the effects of navigation failures for particular actions. By considering these effects when evaluating the actions to take, our framework can adapt navigation strategies to best take advantage of the capabilities of the robot in a given environment. A natural output of our framework is a topological network of actions and switching conditions, providing compact representations of work areas suitable for fast, scalable planning.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144073/1/rgoeddel_1.pd

Mobile Robot Navigation in Indoor Environments: Geometric, Topological, and Semantic Navigation

The objective of the chapter is to show current trends in robot navigation systems related to indoor environments. Navigation systems depend on the level of abstraction of the environment representation. The three main techniques for representing the environment will be described: geometric, topological, and semantic. The geometric representation of the environment is closer to the sensor and actuator world and it is the best one to perform local navigation. Topological representation of the environment uses graphs to model the environment and it is used in large navigation tasks. The semantic representation is the most abstract representation model and adds concepts such as utilities or meanings of the environment elements in the map representation. In addition, regardless of the representation used for navigation, perception plays a significant role in terms of understanding and moving through the environment

Robotic Exploration: Place Recognition as a Tipicality Problem

Autonomous exploration is one of the main challenges of robotic researchers. Exploration requires navigation capabilities in unknown environments and hence, the robots should be endowed not only with safe moving algorithms but also with the ability to recognise visited places. Frequently, the aim of indoor exploration is to obtain the map of the robot’s environment, i.e. the mapping process. Not having an automatic mapping mechanism represents a big burden for the designer of the map because the perception of robots and humans differs significantly from each other. In addition, the loop-closing problem must be addressed, i.e. correspondences among already visited places must be identified during the mapping process. In this chapter, a recent method for topological map acquisition is presented. The nodes within the obtained topologicalmap do not represent single locations but contain information about areas of the environment. Each time sensor measurements identify a set of landmarks that characterise a location, the method must decide whether or not it is the first time the robot visits that location. From a statistical point of view, the problem we are concerned with is the typicality problem, i.e. the identification of new classes in a general classification context. We addressed the problem using the so-called INCA statistic which allows one to perform a typicality test (Irigoien & Arenas, 2008). In this approach, the analysis is based on the distances between each pair of units. This approach can be complementary to the more traditional approach units × measurements – or features – and offers some advantages over it. For instance, an important advantage is that once an appropriate distance metric between units is defined, the distance- based method can be applied regardless of the type of data or the underlying probability distribution

Topological Mapping and Navigation in Real-World Environments

We introduce the Hierarchical Hybrid Spatial Semantic Hierarchy (H2SSH), a hybrid topological-metric map representation. The H2SSH provides a more scalable representation of both small and large structures in the world than existing topological map representations, providing natural descriptions of a hallway lined with offices as well as a cluster of buildings on a college campus. By considering the affordances in the environment, we identify a division of space into three distinct classes: path segments afford travel between places at their ends, decision points present a choice amongst incident path segments, and destinations typically exist at the start and end of routes. Constructing an H2SSH map of the environment requires understanding both its local and global structure. We present a place detection and classification algorithm to create a semantic map representation that parses the free space in the local environment into a set of discrete areas representing features like corridors, intersections, and offices. Using these areas, we introduce a new probabilistic topological simultaneous localization and mapping algorithm based on lazy evaluation to estimate a probability distribution over possible topological maps of the global environment. After construction, an H2SSH map provides the necessary representations for navigation through large-scale environments. The local semantic map provides a high-fidelity metric map suitable for motion planning in dynamic environments, while the global topological map is a graph-like map that allows for route planning using simple graph search algorithms. For navigation, we have integrated the H2SSH with Model Predictive Equilibrium Point Control (MPEPC) to provide safe and efficient motion planning for our robotic wheelchair, Vulcan. However, navigation in human environments entails more than safety and efficiency, as human behavior is further influenced by complex cultural and social norms. We show how social norms for moving along corridors and through intersections can be learned by observing how pedestrians around the robot behave. We then integrate these learned norms with MPEPC to create a socially-aware navigation algorithm, SA-MPEPC. Through real-world experiments, we show how SA-MPEPC improves not only Vulcan’s adherence to social norms, but the adherence of pedestrians interacting with Vulcan as well.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144014/1/collinej_1.pd