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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In this paper, we propose a method for semantic parsing the 3D point cloud of an entire building using a hierarchical approach: first, the raw data is parsed into semantically meaningful spaces (e.g. rooms, etc) that are aligned into a canonical reference coordinate system. Second, the spaces are parsed into their structural and building elements (e.g. walls, columns, etc). Performing these with a strong notation of global 3D space is the backbone of our method. The alignment in the first step injects strong 3D priors from the canonical coordinate system into the second step for discovering elements. This allows diverse challenging scenarios as man-made indoor spaces often show recurrent geometric patterns while the appearance features can change drastically. We also argue that identification of structural elements in indoor spaces is essentially a detection problem, rather than segmentation which is commonly used. We evaluated our method on a new dataset of several buildings with a covered area of over 6, 000m2 and over 215 million points, demonstrating robust results readily useful for practical applications. Surveys the developments of the last 20 years in the area of vision for mobile robot navigation. Two major components of the paper deal with indoor navigation and outdoor navigation. For each component, we have further subdivided our treatment of the subject on the basis of structured and unstructured environments. For indoor robots in structured environments, we have dealt separately with the cases of geometrical and topological models of space. For unstructured environments, we have discussed the cases of navigation using optical flows, using methods from the appearance-based paradigm, and by recognition of specific objects in the environment. Mobile robot vision-based navigation has been the source of countless research contributions, from the domains of both vision and control. Vision is becoming more and more common in applications such as localization, automatic map construction, autonomous navigation, path following, inspection, monitoring or risky situation detection. This survey presents those pieces of work, from the nineties until nowadays, which constitute a wide progress in visual navigation techniques for land, aerial and autonomous underwater vehicles. The paper deals with two major approaches: map-based navigation and mapless navigation. Map-based navigation has been in turn subdivided in metric map-based navigation and topological map-based navigation. Our outline to mapless navigation includes reactive techniques based on qualitative characteristics extraction, appearance-based localization, optical flow, features tracking, plane ground detection tracking, etc... The recent concept of visual sonar has also been revised. Dieses Kapitel gibt eine Einfuhrung in die Kartenerstellung und gleichzeitige Lokalisierung mobiler Sensorplattformen. Die gemeinsame Losung dieser beiden Probleme ist eine Voraussetzung fur die Realisierung vieler technischer Systeme von leichten Fluggeraten uber autonome Roboter bis hin zu mobilen Kameras. Als Simultaneous Localization and Mapping bezeichnet man die Aufgabe, die Trajektorie samt Orientierungsinformation einer sich bewegenden Plattform aus Beobachtungen zu schatzen und gleichzeitig eine Karte der Umgebung zu erstellen. Diese Aufgabe ist in vielen realen Systemen von entscheidender Bedeutung: einerseits stellen hochgenaue Karten mitunter einen Wert an sich fur den Benutzer oder eine spezielle Anwendung dar, andererseits benotigen beispielsweise autonome Roboter ein solches Modell, um zielgerichtet selbststandig navigieren zu konnen. Das Simultaneous Localization and Mapping Problem, beziehungsweise Teilprobleme davon, werden, je nach verwendeter Sensorik, auch als Bundelausgleichung, Structure from Motion oder SLAM bezeichnet.