We designed a new omnidirectional image sensor COPIS (Conic Projection Image Sensor) to guide the navigation of a mobile robot. The feature of COPIS is passive sensing of the omnidirectional image of the environment, in real-time (at the frame rate of a TV camera), using a conic mirror. COPIS is a suitable sensor for visual navigation in a real world environment. We report here a method for navigating a robot by detecting the azimuth of each object in the omnidirectional image. The azimuth is matched with the given environmental map. The robot can precisely estimate its own location and motion (the velocity of the robot) because COPIS observes a 360/spl deg/ view around the robot, even when all edges are not extracted correctly from the omnidirectional image. The robot can avoid colliding against unknown obstacles and estimate locations by detecting azimuth changes, while moving about in the environment. Under the assumption of the known motion of the robot, an environmental map of an indoor scene is generated by monitoring azimuth change in the image. >

MapBased Navigation for a Mobile Robot with Ominidirectional Image Sensor COPIS

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Exploring Artificial Intelligence In The New Millennium

Recent trends in social aware robot navigation

The ability to interpret a scene is an important capability for a robot that is supposed to interact with its environment. The knowledge of what is in front of the robot is, for example, relevant for navigation, manipulation, or planning. Semantic segmentation labels each pixel of an image with a class label and thus provides a detailed semantic annotation of the surroundings to the robot. Convolutional neural networks (CNNs) are popular methods for addressing this type of problem. The available software for training and the integration of CNNs for real robots, however, is quite fragmented and often difficult to use for non-experts, despite the availability of several high-quality open-source frameworks for neural network implementation and training. In this paper, we propose a tool called Bonnet, which addresses this fragmentation problem by building a higher abstraction that is specific for the semantic segmentation task. It provides a modular approach to simplify the training of a semantic segmentation CNN independently of the used dataset and the intended task. Furthermore, we also address the deployment on a real robotic platform. Thus, we do not propose a new CNN approach in this paper. Instead, we provide a stable and easy-to-use tool to make this technology more approachable in the context of autonomous systems. In this sense, we aim at closing a gap between computer vision research and its use in robotics research. We provide an open-source codebase for training and deployment. The training interface is implemented in Python using TensorFlow and the deployment interface provides C++ library that can be easily integrated in an existing robotics codebase, a ROS node, and two standalone applications for label prediction in images and videos.

Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics using CNNs

Semantic segmentation of images is an important issue for intelligent vehicles and mobile robotics because it offers basic information which can be used for complex reasoning and safe navigation. Different solutions have been proposed for this problem along the last two decades, where recent deep neural networks approaches have shown very promising results in the context of urban navigation. One of the main problems when comparing different semantic segmentation solutions is how to select an appropriate metric to evaluate their accuracy. On the one hand, classic metrics do not measure properly the accuracy on the object contours, which is important in urban driving to differentiate road from sidewalk for instance. On the other hand, contour-based metrics [1] disregard the information far from class contours. This paper explores the problem multi-modal image segmentation, and presents a new metric to leverage global and contour accuracy in a simple formulation. This metric is validated with the evaluation of several semantic segmentation solutions that exploit RGB-D images to rank these solutions taking into account the quality of the segmented contours. We also present a comparative analysis of several commonly used metrics together with a statistical analysis of their correlation.

/pdf/a-new-metric-for-evaluating-semantic-segmentation-leveraging-321wtzhbvm.pdf

A New Metric for Evaluating Semantic Segmentation: Leveraging Global and Contour Accuracy

Mimicking human navigation is a challenging goal for autonomous robots. This requires to explicitly take into account not only geometric representation but also high-level interpretation of the environment. In this paper, we demonstrate the capability to infer a route in a global map by using semantics. Our approach relies on an object-based representation of the world automatically built by robots from spherical images. In addition, we propose a new approach to specify paths in terms of high-level robot actions. This path description provides robots with the ability to interact with humans in an intuitive way. We perform experiments on simulated and real-world data, demonstrating the ability of our approach to deal with complex large-scale outdoor environments whilst dealing with labelling errors.

/pdf/semantic-representation-for-navigation-in-large-scale-406u0x9f8d.pdf

Semantic representation for navigation in large-scale environments

Navigation in large scale environments is challeng- ing because it requires accurate local map and global relocation ability. We present a new hybrid metric-topological-semantic map structure, called MTS-map, that allows a fine metric-based navigation and fast coarse query-based localisation. It consists of local sub-maps connected through two topological layers at metric and semantic levels. Semantic information is used to build concise local graph-based descriptions of sub-maps. We propose a robust and efficient algorithm that relies on MTS-map structure and semantic description of sub-maps to relocate very fast. We combine the discriminative power of semantics with the robustness of an interpretation tree to compare the graphs very fast and outperform state-of-the-art-techniques. The proposed approach is tested on a challenging dataset composed of more than 13000 real world images where we demonstrate the ability to relocate within 0.12ms.

/pdf/fast-hybrid-relocation-in-large-scale-metric-topologic-3q790yrr8i.pdf

Fast Hybrid Relocation in Large Scale Metric-Topologic-Semantic Map

Mapping evolving environments requires an update mechanism to efficiently deal with dynamic objects. In this context, we propose a new approach to update maps pertaining to large-scale dynamic environments with semantics. While previous works mainly rely on large amount of observations, the proposed framework is able to build a stable representation with only two observations of the environment. To do this, scene understanding is used to detect dynamic objects and to recover the labels of the occluded parts of the scene through an inference process which takes into account both spatial context and a class occlusion model. Our method was evaluated on a database acquired at two different times with an interval of three years in a large dynamic outdoor environment. The results point out the ability to retrieve the hidden classes with a precision score of 0.98. The performances in term of localisation are also improved.

/pdf/hybrid-metric-topological-semantic-mapping-in-dynamic-4tsgv895q7.pdf

Hybrid metric-topological-semantic mapping in dynamic environments

We present a generative approach to perform robot mapping that is based on an intelligent integration of static and dynamic entity classes within an environment, in order to extrapolate map information at various resolutions. Our framework differentiates from the conventional standpoint where different mapping levels are overlaid on one another, by fusing information from different mapping levels that allows us to infer new information within partially mapped environments. Towards this goal, we develop a class-dependent map extrapolation function that captures the discriminative relation between an environment entity and the mapping procedure. We illustrate the advantages in using heterogeneous contextual information when mapping an environment using a prototype implementation of our approach on an indoor robot platform, giving very promising results.

/pdf/local-map-extrapolation-in-dynamic-environments-2lcfhrqwvp.pdf

Local map extrapolation in dynamic environments

Nous presentons une nouvelle representation hybride metrique/topologique/semantique appelee MTS-Maps concue pour la conduite automatique de vehicule autonome dans des environnements de grande taille d'interieur ou d'exterieur. Basee sur une representation generique, la sphere RGB-DL, elle permet de s'affranchir de la technologie du capteur utilise. Ces differentes couches permettent de traiter le probleme de localisation allant de la requete contextuelle jusqu'au calcul de l'erreur de pose utilise dans le feedback du controleur. Les resultats presentes montrent ses performances a la fois au niveau metrique par une experimentation de conduite automatique dans un environnement urbain et au niveau semantique par une comparaison avec une implementation recente d'une methode Bag-of-Words.

https://hal.archives-ouvertes.fr/hal-00988843/document

Romain Drouilly

Papers

Semantic representation for navigation in large-scale environments

Fast Hybrid Relocation in Large Scale Metric-Topologic-Semantic Map

Hybrid metric-topological-semantic mapping in dynamic environments

Local map extrapolation in dynamic environments

Représentation orientée navigation d'environnements à grande échelle