AQUALOC: An Underwater Dataset for Visual-Inertial-Pressure Localization

We present a new dataset, dedicated to the development of simultaneous localization and mapping methods for underwater vehicles navigating close to the seabed. The data sequences composing this dataset are recorded in three different environments: a harbor at a depth of a few meters, a first archaeological site at a depth of 270 meters and a second site at a depth of 380 meters. The data acquisition is performed using Remotely Operated Vehicles equipped with a monocular monochromatic camera, a low-cost inertial measurement unit, a pressure sensor and a computing unit, all embedded in a single enclosure. The sensors' measurements are recorded synchronously on the computing unit and seventeen sequences have been created from all the acquired data. These sequences are made available in the form of ROS bags and as raw data. For each sequence, a trajectory has also been computed offline using a Structure-from-Motion library in order to allow the comparison with real-time localization methods. With the release of this dataset, we wish to provide data difficult to acquire and to encourage the development of vision-based localization methods dedicated to the underwater environment. The dataset can be downloaded from: http://www.lirmm.fr/aqualoc/Comment: The International Journal of Robotics Research, SAGE Publications, 201

Real-time Monocular Visual Odometry for Turbid and Dynamic Underwater Environments

In the context of robotic underwater operations, the visual degradations induced by the medium properties make difficult the exclusive use of cameras for localization purpose. Hence, most localization methods are based on expensive navigational sensors associated with acoustic positioning. On the other hand, visual odometry and visual SLAM have been exhaustively studied for aerial or terrestrial applications, but state-of-the-art algorithms fail underwater. In this paper we tackle the problem of using a simple low-cost camera for underwater localization and propose a new monocular visual odometry method dedicated to the underwater environment. We evaluate different tracking methods and show that optical flow based tracking is more suited to underwater images than classical approaches based on descriptors. We also propose a keyframe-based visual odometry approach highly relying on nonlinear optimization. The proposed algorithm has been assessed on both simulated and real underwater datasets and outperforms state-of-the-art visual SLAM methods under many of the most challenging conditions. The main application of this work is the localization of Remotely Operated Vehicles (ROVs) used for underwater archaeological missions but the developed system can be used in any other applications as long as visual information is available

Learning Speckle Suppression in Sar Images Without Ground Truth: Application to Sentinel-1 Time-Series

International audienceThis paper proposes a method of denoising SAR images, using a deep learning method, which takes advantage of the abundance of data to learn on large stacks of images of the same scene. The approach is based on the use of convolu-tional networks, used as auto-encoders. Learning is led on a large pile of images acquired on the same area, and assumes that the images of this stack differ only by the speckle noise. Several pairs of images are chosen randomly in the stack, and the network tries to predict the slave image from the master image. In this prediction, the network can not predict the noise because of its random nature. Also the application of this network to a new image fulfills the speckle filtering function. Results are given on Sentinel 1 images. They show that this approach is qualitatively competitive with literature

Apprentissage multi-tâche de l'élévation et de la sémantique à partir d'images aériennes

International audienceAerial or satellite imagery is a great source for land surface analysis, which might yield land use maps or elevation models. In this investigation, we present a neural network framework for learning semantics and local height together. We show how this joint multi-task learning benefits to each task on the large dataset of the 2018 Data Fusion Contest. Moreover, our framework also yields an uncertainty map which allows assessing the prediction of the model. Code is available at https://github.com/marcelampc/mtl_aerial_images

In Honor of Fred Gray: The Meaning of Montgomery

International audienceWe present a new method for depth estimation, based on a stereoscopic camera with various camera focus setting. Depth isestimated using a criterion derived from a maximum likelihood estimator, which jointly analyses the data likelihood with respect to the disparity and the defocus blur of each camera. Benefit of this approach is studied, in particular for scene having repetitive patterns with respect to classical stereoscopy, then we present experimental results on outdoor scenes from a real infra-red stereoscopic system.Nous présentons une nouvelle méthode d’estimation de profondeur, qui repose sur l’utilisation de deux caméras en configuration stéréoscopique avec chacune une mise au point différente. La profondeur est estimée via un critère dérivé d’un maximum de vraisemblance qui intègre conjointement des informations de disparité et les flous de défocalisation de chaque caméra. Nous étudions les apports de cette approche, notamment sur des scènes à motifs répétitifs, par rapport à la stéréoscopie classique sur des images issues de caméras opérant dans le visible.Enfin nous montrons des exemples de résultats sur des images IR thermique acquises en extérieur

Technical Report: Co-learning of geometry and semantics for online 3D mapping

This paper is a technical report about our submission for the ECCV 2018 3DRMS Workshop Challenge on Semantic 3D Reconstruction \cite{Tylecek2018rms}. In this paper, we address 3D semantic reconstruction for autonomous navigation using co-learning of depth map and semantic segmentation. The core of our pipeline is a deep multi-task neural network which tightly refines depth and also produces accurate semantic segmentation maps. Its inputs are an image and a raw depth map produced from a pair of images by standard stereo vision. The resulting semantic 3D point clouds are then merged in order to create a consistent 3D mesh, in turn used to produce dense semantic 3D reconstruction maps. The performances of each step of the proposed method are evaluated on the dataset and multiple tasks of the 3DRMS Challenge, and repeatedly surpass state-of-the-art approaches

Conception conjointe optique/traitement pour un imageur compact à capacité 3D

This thesis is dedicated to the design of a single-frame passive imaging system able to produce a depth map of the observed scene. This design is based on the joint optimisation of the optical and processing parameters, also it is referred to as codesign. The depth estimation ability of a single-frame passive imaging system relies on the depth from defocus principle (DFD) where depth is estimated based on the local estimation of the defocus blur. A new local depth estimation algorithm is developed. It is unsupervised and generic enough to be used with a large family of imaging systems. Then two optical concepts improving depth estimation accuracy are theoretically and experimentally investigated: a coded aperture or a lens with longitudinal chromatic aberration. The realisation of a chromatic imaging system with depth estimation ability is new and this work underlines the advantages of the chromatic solution, in terms of accuracy and range where depth can be estimated. Then a performance model is developed in order to predict the theoretical depth estimation accuracy of any imaging system that uses DFD. This model predicts the accuracy of an imaging system based on the knowledge of the optical and the processing parameters. It is then used as a tool for the design of the first codesigned chromatic imaging system optimised for depth estimation. The realisation of this prototype has highlighted the interest of the codesign approach.Cette thèse traite de la conception d'un imageur monovoie passif capable de produire une carte de profondeur de la scène observée. Cet imageur est conçu en optimisant conjointement les paramètres de l'optique et des traitements, méthode désignée par le terme de co-conception ou de conception conjointe. La capacité 3D de cet imageur repose sur le concept de depth from defocus (DFD) qui consiste à estimer la profondeur à l'aide d'une estimation locale du flou de défocalisation. Ces travaux portent en premier lieu sur le développement d'un algorithme d'estimation locale de profondeur non supervisé et applicable à une famille étendue d'imageurs passifs monovoies. Puis deux concepts d'optique favorisant l'estimation de profondeur sont étudiés, du point de vue théorique et expérimental: l'utilisation d'une pupille codée ou d'une optique avec une aberration chromatique longitudinale non corrigée. La réalisation d'un imageur chromatique à capacité 3D est innovante et permet d'illustrer les avantages de cette solution en termes de précision et de région de l'espace où l'estimation de profondeur est possible. Un modèle de performance est ensuite proposé pour prédire la précision des imageurs utilisant la DFD en fonction des paramètres de l'optique, du capteur et des traitements. Ce modèle est utilisé pour la conception du premier imageur chromatique à capacité 3D co-conçu dont la réalisation a permis d'illustrer l'intérêt de la co-conception