Improving SLAM with Drift Integration

International audienceLocalization without prior knowledge can be a difficult task for a vehicle. An answer to this problematic lies in the Simultaneous Localization And Mapping (SLAM) approach where a map of the surroundings is built while simultaneously being used for localization purposes. However, SLAM algorithms tend to drift over time, making the localization inconsistent. In this paper, we propose to model the drift as a localization bias and to integrate it in a general architecture. The latter allows any feature-based SLAM algorithm to be used while taking advantage of the drift integration. Based on previous works, we extend the bias concept and propose a new architecture which drastically improves the performance of our method, both in terms of computational power and memory required. We validate this framework on real data with different scenarios. We show that taking into account the drift allows us to maintain consistency and improve the localization accuracy with almost no additional cost

Laboratories and Technology: From Temples to Workshops: Sites of Chemistry in Ancient Civilizations

Survey of the workshop equipment in the Babylonian, Egyptian and Graeco-Roman tradition

Real-Time Monocular SLAM With Low Memory Requirements

International audienceThe localization of a vehicle in an unknown environment is often solved using Simultaneous Localization And Mapping (SLAM) techniques. Many methods have been developed , each requiring a different amount of landmarks (map size) , and so of memory , to work efficiently. Similarly , the required computational time is quite variable from one approach to another. In this paper , we focus on the monocular SLAM problem and propose a new method , called MSLAM , based on an Extended Kalman Filter (EKF). The aim is to provide a solution that has low memory and processing time requirements and that can achieve good localization results while benefiting from the EKF advantages (direct access to the covariance matrix , no conversion required for the measures or the state). To do so , a minimal Cartesian representation (3 parameters for 3 dimensions) is used. However , linearization errors are likely to happen with such a representation. New methods allowing to avoid or hugely decrease the impact of the linearization failures are presented. The first contribution proposed here computes a proper projection of a 3D uncertainty in the image plane , allowing to track landmarks during longer periods of time. A corrective factor of the Kalman gain is also introduced. It allows to detect wrong updates and correct them , thus reducing the impact of the linearization on the whole system. Our approach is compared to a classic SLAM implementation over different data sets and conditions so as to illustrate the efficiency of the proposed contributions. The quality of the map built is tested by using it with another vehicle for localization purposes. Finally , a public data set , presenting a long trajectory (1. 3 km) is also used in order to compare MSLAM to a state-of-the-art monocular EKF-SLAM algorithm , both in terms of accuracy and computational needs

Stratégie de perception active pour l'interprétation de scènes : Application à une scène routière

National audienceCet article décrit une méthode générique pour reconnaitre des objets donnés en cherchant à utiliser au mieux toutes les connaissances a priori disponibles de la scène. Chaque objet est composé d'un ensemble de parties. A chacune de ces parties sont associés une primitive et un détecteur pour la trouver. Les différentes étapes de l'approche seront alors : la focalisation des parties (c'est à dire qu'on détermine la zone de recherche des primitives associées), la sélection de la "meilleure partie" (celle qui a priori doit apporter le plus pour la reconnaissance de l'objet), la détection des primitives dans la zone associée à cette partie et la sélection de la meilleure primitive (celle qui correspond le plus à nos attentes) et enfin la mise à jour de l'objet compte tenu de la réussite (ou de l'échec) de la détection précédente. Ce papier décrit cette approche avec une application dédiée à une scène routière comprenant une route et un panneau de limitation de vitesse

Risk-Aware Navigation for Mobile Robots in Unknown 3D Environments

Autonomous navigation in unknown 3D environments is a key issue for intelligent transportation, while still being an open problem. Conventionally, navigation risk has been focused on mitigating collisions with obstacles, neglecting the varying degrees of harm that collisions can cause. In this context, we propose a new risk-aware navigation framework, whose purpose is to directly handle interactions with the environment, including those involving minor collisions. We introduce a physically interpretable risk function that quantifies the maximum potential energy that the robot wheels absorb as a result of a collision. By considering this physical risk in navigation, our approach significantly broadens the spectrum of situations that the robot can undertake, such as speed bumps or small road curbs. Using this framework, we are able to plan safe trajectories that not only ensure safety but also actively address the risks arising from interactions with the environment.Comment: \copyright 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Quelle démarche cognitive pour réaliser une carte mondiale de l’aréisme ?: Les enseignements de la correspondance De Martonne - Aufrère

International audienceAu carrefour de la climatologie, de l’hydrographie et de la géomorphologie, Emmanuel de Martonne commence une carte des régions privées d’écoulement vers l’océan avant 1914, et il la reprend après la guerre, avec la collaboration de Léon Aufrère. Il la présente pour la première fois au congrès international de géographie du Caire (1925). De Martonne a la réputation de se méfier de la théorisation, de pratiquer une recherche inductive fondée sur l’observation de terrain. Comment produire une carte mondiale dans ce cadre cognitif ? Comment pallier l’absence de données précises ou fiables sur beaucoup de portions des continents ? La correspondance entre les deux collaborateurs, encore inédite, permet de saisir cette carte en train de se faire. Elle montre que l’acte cartographique fait appel à plusieurs types d’opérations conjointes de figuration, de catégorisation, de calcul, d’interprétation de cartes intermédiaires. Elle révèle le rôle majeur joué par l’hypothèse climatique dans la construction de la carte comme dans son interprétation. Au total, le processus implique une activité de modélisation par laquelle, progressivement au cours de sa carrière de géographe, de Martonne serait passé de l’usage implicite d’une norme climatique européocentrée à l’adoption d’une règle universelle à référence climatique ou zonale

Effect of Condensed Tannins in Sainfoin on \u3cem\u3ein Vitro\u3c/em\u3e Protein Solubility of Lucerne

Proteins of fresh legume forages such as lucerne are highly degraded in the rumen, resulting in their inefficient use by the animal. The condensed tannins (CT) present in some forages can improve the nutritional value of these forages and of associated feeds in the diet. Previous in vitro work (Waghorn & Shelton, 1997) showed that CT from Lotus corniculatus are able to bind with and precipitate protein from a ryegrass/clover pasture, but when these forages were fed to sheep, the CT effects on digestion and animal performance were weak. This revealed a need for a better understanding of the mechanism of CT interaction between feeds. The present work was designed to measure, in vitro, the effects of CT in sainfoin when mixed with fresh lucerne

Theory and Concepts: The Mythological Foundation of Chemical Theories in Ancient Civilizations

Survey of ancient chemical theories in Egypt, Mesopotamia, and in the Graeco-Roman Worl

Dynamic Lambda-Field: A Counterpart of the Bayesian Occupancy Grid for Risk Assessment in Dynamic Environments

In the context of autonomous vehicles, one of the most crucial tasks is to estimate the risk of the undertaken action. While navigating in complex urban environments, the Bayesian occupancy grid is one of the most popular types of maps, where the information of occupancy is stored as the probability of collision. Although widely used, this kind of representation is not well suited for risk assessment: because of its discrete nature, the probability of collision becomes dependent on the tessellation size. Therefore, risk assessments on Bayesian occupancy grids cannot yield risks with meaningful physical units. In this article, we propose an alternative framework called Dynamic Lambda-Field that is able to assess generic physical risks in dynamic environments without being dependent on the tessellation size. Using our framework, we are able to plan safe trajectories where the risk function can be adjusted depending on the scenario. We validate our approach with quantitative experiments, showing the convergence speed of the grid and that the framework is suitable for real-world scenarios.Comment: 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work