Qualitative localization using vision and odometry for path following in topo-metric maps

International audienceWe address the problem of navigation in topo- metric maps created by using odometry data and visual loop- closure detection. Based on our previous work [6], we present an optimized version of our loop-closure detection algorithm that makes it possible to create consistent topo-metric maps in real-time while the robot is teleoperated. Using such a map, the proposed navigation algorithm performs qualitative localization using the same loop-closure detection framework and the odometry data. This qualitative position is used to support robot guidance to follow a predicted path in the topo-metric map compensating the odometry drift. Compared to purely visual servoing approaches for similar tasks, our path-following algorithm is real-time, light (not more than two images per seconds are processed), and robust as odometry is still available to navigate even if vision information is absent for a short time. The approach has been validated experimentally with a Pioneer P3DX robot in indoor environments with embedded and remote computations

Automatic Underwater Image Denoising

A novel pre-processing filter is proposed for underwater image restoration. Because of specific transmission properties of light in the water, underwater image suffers from limited range,non uniform lighting, low contrast, color diminished, important blur… Today pre-processing methods typically only concentrates on non uniform lighting or color correction and often require additional knowledge of the environment. The algorithm proposed in this paper is an automatic algorithm to pre-process underwater images. It reduces underwater perturbations, and improves image quality. It is composed of several successive independent processing steps which correct non uniform illumination, suppress noise, enhance contrast and adjust colors. Performances of filtering will be assessed using an edge detection robustness criterion.L'obstacle majeur dans le traitement des images sous-marines résulte des phénomènes d'absorption et de diffusion dus aux propriétés optiques particulières de la lumière dans l'eau. Ces deux phénomènes auxquels s'ajoute le problème de turbidité, impose de travailler sur des images très bruitées, avec souvent, une illumination non uniforme, des contrastes faibles, des couleurs atténuées… Cet article présente une nouvelle méthode automatique de pré-traitement des images sous marines. L'algorithme proposé qui ne nécessite ni paramétrage manuel ni information a priori, permet d'atténuer les défauts précédemment cités et d'améliorer de façon significative la qualité des images. L'éclairage, le bruit, les contrastes puis les couleurs sont corrigés séquentiellement

Incremental topo-metric SLAM using vision and robot odometry

Abstract — We address the problem of simultaneous localization and mapping by combining visual loop-closure detection with metrical information given by the robot odometry. The proposed algorithm builds in real-time topo-metric maps of an unknown environment, with a monocular or omnidirectional camera and odometry gathered by motors encoders. A dedicated improved version of our previous work on purely appearance-based loop-closure detection [1] is used to extract potential loop-closure locations. Potential locations are then verified and classified using a new validation stage. The main contributions we bring are the generalization of the validation method for the use of monocular and omnidirectional camera with the removal of the camera calibration stage, the inclusion of an odometry-based evolution model in the Bayesian filter which improves accuracy and responsiveness, and the addition of a consistent metric position estimation. This new SLAM method does not require any calibration or learning stage (i.e. no a priori information about environment). It is therefore fully incremental and generates maps usable for global localization and planned navigation. This algorithm is moreover well suited for remote processing and can be used on toy robots with very small computational power

A Light Visual Mapping and Navigation Framework for Low-Cost Robots

International audienceWe address the problems of localization, mapping, and guidance for robots with limited computational resources by combining vision with the metrical information given by the robot odometry. We propose in this article a novel light and robust topometric simultaneous localization and mapping framework using appearance-based visual loop-closure detection enhanced with the odometry. The main advantage of this combination is that the odometry makes the loop-closure detection more accurate and reactive, while the loop-closure detection enables the long-term use of odometry for guidance by correcting the drift. The guidance approach is based on qualitative localization using vision and odometry, and is robust to visual sensor occlusions or changes in the scene. The resulting framework is incremental, real-time, and based on cheap sensors provided on many robots (a camera and odometry encoders). This approach is, moreover, particularly well suited for low-power robots as it is not dependent on the image processing frequency and latency, and thus it can be applied using remote processing. The algorithm has been validated on a Pioneer P3DX mobile robot in indoor environments, and its robustness is demonstrated experimentally for a large range of odometry noise levels

A purely model-based approach to object pose and size estimation with electric sense

International audienceIn the 50s, biologists discovered that some electric fish are capable of discriminating the pose as well as the electric and geometric properties of surrounding objects by navigating and measuring the distortions of a self-generated electric field. In this paper, we address the challenging issue of ellipsoidal objects pose and size estimation for underwater robots equipped with artificial electric sense. Unlike current methods, the approach can estimate both the position and size in parallel with a single straight trajectory. No multi-polarization nor reactive self-alignment control are necessary to locate the object. The approach is a purely model-based heuristic that selects the best ellipsoid parameters among a set of potential candidates. It is based on a set of 4 electric measurements recorded at several positions along the robot trajectory along which the displacement is measured. The efficiency of the method is assessed over numerous experiments with different objects, several positions, and orientations, and two different kinds of water (fresh and salt water). Despite some model simplifications and experimental errors, location and size estimation errors are on average below 1cm and 15% respectively, while offering promising perspectives for real-time computation

Underwater Robots Equipped with Artificial Electric Sense for the Exploration of Unconventional Aquatic Niches

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Combined Vision and Frontier-Based Exploration Strategies for Semantic Mapping

Abstract. We present an approach to multi-objective exploration whose goal is to autonomously explore an unknown indoor environment. Our objective is to build a semantic map containing highlevel information, namely rooms and the objects laid in these rooms. This approach was developed for the Panoramic and Active Camera for Object Mapping (PACOM) 1 project in order to participate in a French exploration and mapping contest called CAROTTE 2. To achieve efficient exploration, we combine two classical approaches: frontier-based exploration for 2D laser metric mapping and nextbest view computation for visual object search. Based on a stochastic sampling strategy, this approach looks for a position that maximizes a multi-objective cost function. We show the advantage of using this combined approach compared to each particular approach in isolation. Additionally, we show how an uncertainty reduction strategy makes it possible to reduce object localization error after exploration