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 Bayesian inference and Markov Chain Monte-Carlo sampling to infer optical visibility from point clouds. The visibility estimation is cast as a classification problem based on the identification of the distance distributions. Contrary to deep learning methods, our approach is model-based and focuses on the design of a full probabilistic framework, more comprehensible, which is critical for autonomous driving. Ultimately, the impact of the optical visibility on the probability of detection of standard targets is assessed, which can yield improvements on autonomous vehicles performances in adverse weather conditions.

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 is presented. The proposed architecture is applied to simulated UAV coverage path planning trajectories and an analysis of the approaches strengths and shortcomings is provided.

Simulation is very helpful for the development of cyber-physical systems, as it enables testing functionalities and their integration without full hardware deployment. For complex systems, such as fleets of heterogeneous robots, multiple simulators dedicated to particular physical processes must be interconnected, so as to build a wholesome simulation and test the overall system. A key property to ensure is that the overall simulation is repeatable. We propose a lightweight distributed architecture for time management, allowing to easily deploy complex simulations while strictly ensuring repeatability. A formal model of the architecture is provided, along with a proof of progress. An open source implementation, with a binding to the robotic ROS framework is made available.

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 measurements; ii) designing a planning algorithm which exploits the obtained map to generate trajectories that optimize the adaptive data sampling process, minimizing the uncertainty in the map, while steering the vehicles within the air flows to generate energetic-efficient flights. Our approach is based on Gaussian Processes (GP) for the mapping, combined with a stochastic optimization scheme for the trajectories generation. The system is tested in simulations carried out using a realistic three-dimensional current field. Results for a single UAV as well as for a fleet of multiple UAVs, sharing information to cooperatively achieve the mission, are provided.

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 of the perceived surfaces, and is not strongly impacted by the scene illumination - contrary to visual information. This article investigates how such information can augment the precision of a point cloud classifier. It presents an empirical evaluation of a low cost LiDAR, introduces features related to the intensity and multiple echoes and their use in a hierarchical classification scheme. Results on varied outdoor scenes are depicted, and show that more precise class identification can be achieved using the intensity and multiple echoes than when using only geometric features.