17 accepted papers
A recent advance in vector field path following is the introduction of the Parametric Guiding Vector Field method. It allows for singularity-free vector fields with strong convergence guarantees, usable even for self-intersecting paths. However, the method requires significant gain tuning for practi…
Trans-media robots, capable of operating across diverse environments, add significant complexity for multi-robot task allocation and planning problems. This paper introduces a novel approach to plan missions for such multi-robot systems, that addresses the associated specific complexities and constr…
Degraded visual environments have strong impacts on the quality of LiDAR data. Experiments in artificial fog conditions show that noise points caused by water particles present various distance distributions which depend on visibility. This article introduces a mathematical framework based on Bayesi…
Covariance estimation for the Iterative Closest Point (ICP) point cloud registration algorithm is essential for state estimation and sensor fusion purposes. We argue that a major source of error for ICP is in the input data itself, from the sensor noise to the scene geometry. Benefiting from recent…
We propose a decentralized approach that simultaneously allocates and decomposes high level tasks among various robots. The approach exploits HTN structures and algorithms, that are used within an auction-based allocation scheme, and aims at dealing with complex tasks with causal or temporal relatio…
This work presents the deployment of UAVs for the exploration of clouds, from the system architecture and simulation tests to a real-flight campaign and trajectory analyzes. Thanks to their small size and low altitude, light UAVs have proven to be adapted for in-situ cloud data collection. The short…
Following recent developments, this paper investigates the possibility to predict uncertainty models for monocular graph SLAM using topological features of the problem. An architecture to learn relative (i.e. inter-keyframe) uncertainty models using the resistance distance in the covisibility graph…
This letter fosters the idea that deep learning methods can be used to complement classical visual odometry pipelines to improve their accuracy and to associate uncertainty models to their estimations. We show that the biases inherent to the visual odometry process can be faithfully learned and comp
Field multi-robot missions face numerous unavoidable disturbances, such as delays in executing tasks and intermittent communications. Coping with such disturbances requires to endow the robots with high-level decision skills. We present a distributed decision architecture based first on a hybrid pla…
We present an approach to plan trajectories for a fleet of fixed-wing UAVs to observe a wildfire evolving over time. Realistic models of the terrain, of the fire propagation process, and of the UAVs are exploited, together with a model of the wind. The approach tailors a generic Variable Neighborhoo…
The complexity of robotics comes from the tight interactions between hardware, complex softwares, and environments. While real world experience is the only way to assess the efficiency and robustness of a robotics system, simulations help to pave the way to actual experiments. But an overall robotic…
Environment models are the primary matter to autonomous decisions for mobile robots, and also to cooperation within teams of robots that operate in the same environment. The decisions to take within a robot or a robot team relate to motions, perceptions and communications: various types of environme…
We study the problem of monitoring the evolution of atmospheric variables within low-altitude cumulus clouds with a fleet of Unmanned Aerial Vehicles (UAVs). To tackle this challenge, two main problems can be identified: i) creating on-line maps of the relevant variables, based on sparse local measu…
A novel method for visual place recognition is introduced and evaluated, demonstrating robustness to perceptual aliasing and observation noise. This is achieved by increasing discrimination through a more structured representation of visual observations. Estimation of observation likelihoods are bas…
Besides precise and dense geometric information, some LiDARs also provide intensity information and multiple echoes, information that can advantageously be exploited to enhance the performance of the purely geometric classification approaches. This information indeed depends on the physical nature o…
With the growing demand for deployment of robots in real scenarios, robustness in the perception capabilities for navigation lies at the forefront of research interest, as this forms the backbone of robotic autonomy. Existing place recognition approaches traditionally follow the feature-based bag-of…
This article depicts an algorithm which matches the output of a Lidar with an initial terrain model to estimate the absolute pose of a robot. Initial models do not perfectly fit the reality and the acquired data set can contain an unknown, and potentially large, proportion of outliers. We present an…