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

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

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

Localization on a vehicle on a precise road map

This article deals with a multisensor based vehicle localization method. The final precision is lesser than one meter. A low cost GPS, a video gray level camera, an odometer and a steer angle sensor provide the data to be fused. The important contributions of the article concern (1) the data fusion by Kalman filtering, (2) the caracterisation of GPS errors and their modelisation by a bias and a low level additive noise and consequently the estimation of the bias and (3) a vision/map coupling to transform local positioning given by a computer vision algorithm into a global reference thus creating another kind of exteroceptive data. The article ends presenting an important experimental validation that corresponds to the implementation of the method in a real driving situation.Cet article présente une approche de fusion multicapteurs permettant d'obtenir la localisation d'un véhicule avec une précision décimétrique. Les différentes sources d'informations utilisées proviennent d'un GPS autonome bas coût, d'une caméra, d'un odomètre et d'un capteur d'angle au volant. Les contributions importantes concernent (1) la formalisation et la résolution du problème de fusion par filtrage de Kalman, (2) la caractérisation expérimentale des erreurs sur les données GPS et consécutivement leur modélisation par un biais et un bruit blanc gaussien additif et l'estimation du biais, (3) le couplage d'une localisation locale par vision avec une carte précise pour fournir une autre source de donnée extéroceptive. L'article se termine par une importante validation expérimentale de la méthode proposée en situation réelle

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

Structure and flexibility in cortical representations of odour space

The cortex organizes sensory information to enable discrimination and generalization1-4. As systematic representations of chemical odour space have not yet been described in the olfactory cortex, it remains unclear how odour relationships are encoded to place chemically distinct but similar odours, such as lemon and orange, into perceptual categories, such as citrus5-7. Here, by combining chemoinformatics and multiphoton imaging in the mouse, we show that both the piriform cortex and its sensory inputs from the olfactory bulb represent chemical odour relationships through correlated patterns of activity. However, cortical odour codes differ from those in the bulb: cortex more strongly clusters together representations for related odours, selectively rewrites pairwise odour relationships, and better matches odour perception. The bulb-to-cortex transformation depends on the associative network originating within the piriform cortex, and can be reshaped by passive odour experience. Thus, cortex actively builds a structured representation of chemical odour space that highlights odour relationships; this representation is similar across individuals but remains plastic, suggesting a means through which the olfactory system can assign related odour cues to common and yet personalized percepts