Climate change is intensifying precipitation events, leading to increased flooding and landslides, particularly in urban areas. Traditional Numerical Weather Prediction (NWP) models are essential for forecasting these events, but require extensive computational resources for high spatio-temporal resolution. Machine Learning (ML) approaches offer a more computationally efficient alternative, with fast prediction phases suitable for less powerful resources, while intensive training can be done asynchronously. We propose a deep learning architecture that incorporates an information data fusion module prior to a CNN-based decision module to reduce the input size while maintaining prediction accuracy. By testing the application of two different subspace methods, Principal Component Analysis decomposition (PCA) and Maximum pixel map, we reduce the number of input channels needed for the decision module. Experiments on the rainfall dataset compare the performance of the decision module with and without the fusion module. The experiments conclude that while the baseline original SmaAt-UNet remains the most reliable, the Fusion Module Max Pixel Map provides a substantial input compression while maintaining performance. This could enable the execution of forecasts on devices with reduced processing capabilities, such as mobile devices.
The poses derived from automatic point cloud registration between stationary laser scans that occur naturally in robotic multi-sensor systems operating in stop-and-go mode have the potential to greatly improve and aid localization and trajectory estimation. The uncertainty assessment of these registered poses is crucial for its correct system integration and utilization, e.g., for mobile mapping applications. However, obtaining ground truth data with sufficient accuracy for both the position and attitude remains challenging. In this paper, we present a novel measurement setup and a data fusion method based on an iterative weighted least squares adjustment to assess the quality of such derived relative poses. The approach is tested and demonstrated in an outdoor experiment covering a driving area of approximately 65 x 7 m. A total station and a laser tracker were used to realize the ground truth. The latter was mounted on a Husky A200 UGV together with a Riegl VZ-600i laser scanner. The realized ground truth enables the determination of the relative poses with respect to a superordinate system with 0.3 mm and 0.04 mrad on average. The relative poses of the automatic registration differ from the reference poses up to a maximum of 3 mm and 0.76 mrad.
In autonomous robotics, a critical challenge lies in developing robust solutions for Active Collaborative SLAM, wherein multiple robots collaboratively explore and map an unknown environment while intelligently coordinating their movements and sensor data acquisitions. In this article, we present an efficient centralized frontier sharing approach that maximizes exploration by taking into account information gain in the merged map, distance, and reward computation among frontier candidates and encourages the spread of agents into the environment. Eventually, our method efficiently spreads the robots for maximum exploration while keeping SLAM uncertainty low. Additionally, we also present two coordination approaches, synchronous and asynchronous to prioritize robot goal assignments by the central server. The proposed method is implemented in ROS and evaluated through simulation and experiments on publicly available datasets and similar methods, rendering promising results.
The Fisher information number (FIN) has previously been proposed as a regularizer to fit a probability density function to a set of constraints. Especially for mixture densities, this is not straightforward and often a reformulation based on square root densities is used. As it is generally much harder to derive the square root of a mixture than squaring it, this only allows for constraints that can be expressed through the root density's parameters. An important case not covered by this are constraints on individual components of a mixture. This paper proposes three methods to approximate the FIN of mixture models: Gauss-Hermite quadrature, polynomial approximation of the square root function, and direct approximation of the square root density of a pdf. This allows using the FIN for smooth density estimation in situations existing methods cannot handle. The three methods are applied to the problem of kernel density estimation with Gaussian kernels and the results are compared.
Magnetic field vector measurement model derived from a simple geometric coil model has been widely reported. These field vector measurements can be used to estimate the pose of a vehicle at a high level of accuracy in real-time. In our previous work, we introduced a magnetic positioning system (MPS) using these magnetic field vector measurements and performed 2D pose estimation. In this paper, we first introduce the initial calibration of the magnetic field considering both the magnetic field measurements and theoretical models. Then, estimation of position, velocity, and attitude is performed by multi sensor fusion between calibrated MPS, odometer and INS. This is implemented in a tightly-coupled(TC) extended Kalman filter(EKF) structure. The results demonstrated precise three- dimensional navigation performance and the benefits of the tightly coupled structure, resulting in accurate navigation and robustness.
