Exploiting Points and Lines in Regression Forests for RGB-D Camera Relocalization

Camera relocalization plays a vital role in many robotics and computer vision tasks, such as global localization, recovery from tracking failure and loop closure detection. Recent random forests based methods exploit randomly sampled pixel comparison features to predict 3D world locations for 2D image locations to guide the camera pose optimization. However, these image features are only sampled randomly in the images, without considering the spatial structures or geometric information, leading to large errors or failure cases with the existence of poorly textured areas or in motion blur. Line segment features are more robust in these environments. In this work, we propose to jointly exploit points and lines within the framework of uncertainty driven regression forests. The proposed approach is thoroughly evaluated on three publicly available datasets against several strong state-of-the-art baselines in terms of several different error metrics. Experimental results prove the efficacy of our method, showing superior or on-par state-of-the-art performance.Comment: published as a conference paper at 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

Habitats et peuplements tardiglaciaires du Bassin parisien. Projet collectif de recherche 2003-2005

Ce rapport d'activités annuel contient les contributions d'une trentaine de chercheurs œuvrant à l'étude des sociétés qui vécurent dans le Bassin parisien pendant le Tardiglaciaire (XIVe-Xe millénaire avant J.-C.). Analyses en cours sur l'environnement, recension de nouveaux sites archéologiques, études sur la culture matérielle et les pratiques de chasse illustrent divers projets en cours

Learning to Navigate the Energy Landscape

In this paper, we present a novel and efficient architecture for addressing computer vision problems that use `Analysis by Synthesis'. Analysis by synthesis involves the minimization of the reconstruction error which is typically a non-convex function of the latent target variables. State-of-the-art methods adopt a hybrid scheme where discriminatively trained predictors like Random Forests or Convolutional Neural Networks are used to initialize local search algorithms. While these methods have been shown to produce promising results, they often get stuck in local optima. Our method goes beyond the conventional hybrid architecture by not only proposing multiple accurate initial solutions but by also defining a navigational structure over the solution space that can be used for extremely efficient gradient-free local search. We demonstrate the efficacy of our approach on the challenging problem of RGB Camera Relocalization. To make the RGB camera relocalization problem particularly challenging, we introduce a new dataset of 3D environments which are significantly larger than those found in other publicly-available datasets. Our experiments reveal that the proposed method is able to achieve state-of-the-art camera relocalization results. We also demonstrate the generalizability of our approach on Hand Pose Estimation and Image Retrieval tasks

Valorisation of glycerol by new mechanochemical processes

The search for new applications of glycerol, as a chemical platform from which a broad spectrum of new valuable derivatives can be obtained, is ongoing. In the present communication, a new mechano-chemical reactor is used for the valorisation of glycerol, and some examples of potential chemical processes by using mechano-chemical energy will be provided in order to reduce the residence time, to minimize the use of solvents or to decrease the temperature. In this sense, the mechano-chemical synthesis of calcium diglyceroxide from glycerol and CaO has been optimised. Finally, a new and more efficient mechano-chemical synthesis of CaDG has been achieved, requiring short synthesis time without heating and no need of solvents. The stability of this catalyst is studied under presence of free fatty acids and water, compounds presents in waste oils that decrease the yield to fatty acid methyl ester (FAME) during the reaction. Moreover, the transesterification reaction of used and refined vegetable oils with methanol has also been studied and optimised in the presence of CaDG as basic solid catalyst, using the same mechano-chemical reactor that promotes the oil-methanol mixing, minimizing the mass transfer problems associated to the immiscibility of reactants. Low methanol:oil ratios and low temperature can be used with promising results using a mechanical reactor even with used oils and in plant pilot scale under flow conditions. Glycerol carbonate is a green chemical glycerol derivative with several industrial applications (solvents, pharmaceutics, detergent, adhesives, lubricants, beauty, among others). Preliminary tests using a mechano-chemical reactor under continuous flow conditions shows the possibility to reduce the time of reaction to 1h and lowering the temperature. Finally, the production of Zn glycerolate (good candidate for the tire industry) is also studied.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

An efficient and sustainable biodiesel production in a mechanochemical pilot reactor under heterogeneous catalysis

Fatty acid methyl esters (FAME) produced from vegetable oils or animal fats by transesterification, or from the esterification of fatty acids, with methanol, is labeled as ‘‘Biodiesel”. Current industrial processes for biodiesel production are mainly based on homogeneous catalysis, in presence of an alkali hydroxide or methoxide dissolved in methanol, a large excess of methanol (methanol:oil molar ratio > 6), a temperature around 60 ºC and 1-2 h of reaction. However, this process suffers from different drawbacks, mainly related with the generation of large amount of wastewater associated to the washing and neutralization steps, the non-recovery of the homogeneous catalyst, or the formation of stable emulsions difficult to separate. These problems cause an increase of the overall biodiesel production cost. To overcome them, different approaches have been proposed, such as the use of heterogeneous catalysis, CO2 under supercritical conditions or enzymes, coupled to microwave and ultrasonic systems as alternative to conventional heating. In the present communication, a new mechanochemical reactor is used for the transesterification reaction that promotes the oil-methanol mixing, minimizing the mass transfer problems associated to the immiscibility of reactant mixtures. Moreover, in order to achieve a more sustainable biodiesel production process, a new heterogeneous basic catalyst is prepared from calcium oxide and glycerol, the by-product of biodiesel industry.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Mechanochemistry for a smart and sustainable biodiesel production under heterogeneous catalysis

Fatty acid methyl esters (FAME) produced from vegetable oil by transesterification, labeled as ‘‘Biodiesel”, is industrially accomplished in the presence of a homogeneous basic catalyst, such as alkali hydroxide or methoxide dissolved in methanol. This process requires a large excess of methanol (methanol:oil molar ratio> 6), temperature around 60 ºC and 1-2 h of reaction. However, this process suffers from important drawbacks: low FFA and water tolerance, generation of process wastewater, etc. To overcome them, different approaches have been proposed: such as the use of heterogeneous catalysis, CO2 under supercritical conditions or enzymes; coupled to microwave and ultrasonics systems as an alternative to conventional heating. Among all the researches, heterogeneous catalysts show potential in the transesterification reaction. Unlike homogeneous catalysts, heterogeneous ones are environmentally benign and can be reused and regenerated. Nevertheless, higher catalyst loading and alcohol:oil molar ratio are required for biodiesel production in the presence of solid catalysts. A new mechanochemical reactor is used for the transesterification reaction to promotes the reactants mixing, minimizing mass transfer limitations associated to the inmiscibility of reactants. This solution allows to reduce the methanol need to an amount close to the stoichiometry (methanol:oil molar ratio= 4:1), and at room temperature after less than one minute, more than 90 wt% FAME is reached. Glycerol, obtained as by-product in the transesterification reaction is used to prepare calcium diglyceroxide by mechanosynthesis, and is used as heterogeneous catalyst. A new and more efficient mechanochemical synthesis of FAME is proposed, with shorter reaction and lower temperature, compared to other synthesis proposed in literature.Universidad de Málaga.Campus de Excelencia Internacional Andalucía Tec

Stabilizer Inactivation for Message-Passing Decoding of Quantum LDPC Codes

We propose a post-processing method for message-passing (MP) decoding of CSS quantum LDPC codes, called stabilizer-inactivation (SI). It relies on inactivating a set of qubits, supporting a check in the dual code, and then running the MP decoding again. This allows MP decoding to converge outside the inactivated set of qubits, while the error on these is determined by solving a small, constant size, linear system. Compared to the state of the art post-processing method based on ordered statistics decoding (OSD), we show through numerical simulations that MP-SI outperforms MP-OSD for different quantum LDPC code constructions, different MP decoding algorithms, and different MP scheduling strategies, while having a significantly reduced complexity

Real-Time RGB-D Camera Pose Estimation in Novel Scenes using a Relocalisation Cascade

Camera pose estimation is an important problem in computer vision. Common techniques either match the current image against keyframes with known poses, directly regress the pose, or establish correspondences between keypoints in the image and points in the scene to estimate the pose. In recent years, regression forests have become a popular alternative to establish such correspondences. They achieve accurate results, but have traditionally needed to be trained offline on the target scene, preventing relocalisation in new environments. Recently, we showed how to circumvent this limitation by adapting a pre-trained forest to a new scene on the fly. The adapted forests achieved relocalisation performance that was on par with that of offline forests, and our approach was able to estimate the camera pose in close to real time. In this paper, we present an extension of this work that achieves significantly better relocalisation performance whilst running fully in real time. To achieve this, we make several changes to the original approach: (i) instead of accepting the camera pose hypothesis without question, we make it possible to score the final few hypotheses using a geometric approach and select the most promising; (ii) we chain several instantiations of our relocaliser together in a cascade, allowing us to try faster but less accurate relocalisation first, only falling back to slower, more accurate relocalisation as necessary; and (iii) we tune the parameters of our cascade to achieve effective overall performance. These changes allow us to significantly improve upon the performance our original state-of-the-art method was able to achieve on the well-known 7-Scenes and Stanford 4 Scenes benchmarks. As additional contributions, we present a way of visualising the internal behaviour of our forests and show how to entirely circumvent the need to pre-train a forest on a generic scene.Comment: Tommaso Cavallari, Stuart Golodetz, Nicholas Lord and Julien Valentin assert joint first authorshi