Although a good variety of successful LiDAR-based mapping schemes have been developed, these methods present shortcomings when mapping geometrically poor environments. In these scenarios, the chosen map structure and the consideration of map uncertainty are particularly relevant for providing a robust robot motion estimation, critically affecting the quality of the resulting map. This paper introduces the use of probabilistic 3D triangle meshes in LiDAR-based mapping. Our approach combines: i) meshes, which consistently represent planar surfaces and enable the use of decimation techniques to reduce the influence of the measurement noise in the map and improve map fidelity, while strongly reducing the map size; with ii) a probabilistic on-manifold formulation of planar objects, which naturally reflects the measurement uncertainty in the mesh map avoiding inconsistencies in state estimation. The proposed methods are experimentally evaluated both individually and jointly integrated in a generic mapping scheme in different scenarios, showing the improvement in robustness and accuracy in geometrically poor environments and providing strong reductions in map size over existing schemes. We release the used datasets and C++ implementations of the proposed methods.

The need for robots autonomously navigating in more and more complex environments has motivated intense R& D efforts in making robot pose estimation more accurate and reliable. This paper presents a multi-sensor multi-hypothesis method for robust 6-DoF localization in complex environments. Robustness and accuracy requirements are addressed as follows. First, camera and LIDAR features are seamlessly integrated in the same statistical framework, benefiting from their synergies and providing robustness in scenarios with low or varying densities of LIDAR and visual features. Second, a multi-hypothesis approach is adopted to cope with scenario symmetries. The method has been carefully designed to operate in real time using feature and hypothesis filtering and efficient hypothesis refinement, and has been coded in a multi-core implementation. The proposed method has been extensively validated for closed-loop aerial robot navigation in different urban and industrial scenarios and has shown advantages over well-known single-sensor techniques.

There is a high demand in advanced fault detection methods suitable for sensor networks monitoring complex dynamic systems such as industrial plants or large infrastructure units. This paper proposes a robust and efficient decentralized sensor fault detection method with in-place sensor self-recalibration capability that extracts and uses complex context information referred to the full monitored process. The method includes three main components, all decentralized and sharing the same statistical framework: 1) a consensus-based modeling step based on decentralized RANSAC; 2) a statistical analysis based on Bayesian networks and Hidden Markov Models in which each sensor identifies inconsistencies with the consensus model and determines if it is correctly calibrated, uncalibrated or faulty and; 3) a final step in which each uncalibrated sensor self-recalibrates using the consensus model. The proposed method is efficient in the use of computational and communicational resources, it is scalable and robust against outliers, transmission errors, sensor failures and network topology changes. It has been extensively validated in an experimental industrial setting.