Learned Neural Network based policies have shown promising results for robot navigation. However, most of these approaches fall short of being used on a real robot -- they require extensive training in environments, most of which do not simulate the visuals and the dynamics of the real world well enough that the resulting policies can be easily deployed. We present a novel Neural Net based policy, , which allows for easy deployment on a real robot. It consists of two sub policies -- a high level policy which can understand real images and perform long range planning expressed in high level commands; a low level policy that can translate the long range plan into low level commands on a specific platform in a safe and robust manner. For every new deployment, the high level policy is trained on an easily obtainable scan of the environment modeling its visuals and layout. We detail the design of such an environment and how one can use it for training a final navigation policy. Further, we demonstrate a learned low-level policy. We deploy the model in a large office building and test it extensively, achieving @math success rate over long navigation runs and outperforming SLAM-based models in the same settings.
As a very old problem, there is an extensive literature on SLAM, planning and robot navigation @cite_1 @cite_32 @cite_23 @cite_5 , which due to space limitations will not be discussed here. It suffices to say though, that our work falls roughly into mapless navigation. The neural policy requires neither a geometric map of the environment nor localization at test time.
@cite_5 @cite_1 @cite_32 @cite_23
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This thesis describes a scalable robotic navigation system that builds a map of the robot's environment on the fly. This problem is also known as Simultaneous Localization and Mapping (SLAM). The SLAM problem has as inputs the control of the robot's motion and sensor measurements to features in the environment. The desired output is the path traversed by the robot (localization) and a representation of the sensed environment (mapping). The principal contribution of this thesis is the introduction of a framework, termed Atlas, that alleviates the computational restrictions of previous approaches to SLAM when mapping extended environments. The Atlas framework partitions the SLAM problem into a graph of submaps, each with its own coordinate system. Furthermore, the framework facilitates the modularity of sensors, map representations, and local navigation algorithms by encapsulating the implementation specific algorithms module that matches submaps and a verification procedure that trades latency in loop closing with a lower chance of incorrect loop detections inherent with symmetric environments. The framework is demonstrated with several datasets that map large indoor and urban outdoor environments using a variety of sensors: a laser scanner, sonar rangers, and omni-directional video. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.) 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. The maps that are built by standard feature-based simultaneous localization and mapping (SLAM) methods cannot be directly used to compute paths for navigation, unless enriched with obstacle or traversability information, with the consequent increase in complexity. Here, we propose a method that directly uses the Pose SLAM graph of constraints to determine the path between two robot configurations with lowest accumulated pose uncertainty, i.e., the most reliable path to the goal. The method shows improved navigation results when compared with standard path-planning strategies over both datasets and real-world experiments. Autonomous navigation, which consists of a systematic integration of localization, mapping, motion planning and control, is the core capability of mobile robotic systems. However, most research considers only isolated technical modules. There exist significant gaps between maps generated by SLAM algorithms and maps required for motion planning. This paper presents a complete online system that consists in three modules: incremental SLAM, real-time dense mapping, and free space extraction. The obtained free-space volume (i.e. a tessellation of tetrahedra) can be served as regular geometric constraints for motion planning. Our system runs in real-time thanks to the engineering decisions proposed to increase the system efficiency. We conduct extensive experiments on the KITTI dataset to demonstrate the run-time performance. Qualitative and quantitative results on mapping accuracy are also shown. For the benefit of the community, we make the source code public.