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Item type:Publication, Access status: without files , Predicting against the Flow : Boosting Source Localization by Means of Field Belief Modeling using Upstream Source Proximity(IEEE, 2024-08-08); ; ; ; Time-effective and accurate source localization with mobile robots is crucial in safety-critical scenarios, e.g. leakage detection. This becomes particular challenging in realistic cluttered scenarios, i.e. in the presence of complex current flows or wind. Traditional methods often fall short due to simplifications or limited onboard resources. We propose to combine source localization with a Gaussian Markov Random Field (GMRF). This allows to improve source localization hypotheses by building on the GMRF's concentration and flow field belief that are continuously updated by gathered measurements. We introduce the upstream source proximity (USP) as a natural metric that exploits the joint knowledge represented in the field belief's concentration and flow field, i.e. predicting sources upstream. As a result, our method yields a computationally efficient source localization and field belief module providing substantially more stable gradients than conventional concentration gradient-based methods.We demonstrate the suitability of our approach in a series of numerical experiments covering complex source location scenarios. With regard to computational requirements, the method achieves update rates of 10Hz on a RaspberryPi4B.Publicationtype: Conference Paper35 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , AirFisheye Dataset : A Multi-Model Fisheye Dataset for UAV ApplicationsDrone applications require perception all around the vehicle to avoid obstacles during navigation. Due to the weight and computation limitations on UAVs, using a large number of sensors, such as numerous cameras, could be prohibitive. In such scenarios, usage of fisheye cameras with a wider field of view is very beneficial. Despite the usefulness of fisheye camera for UAV applications, not much work has been carried out to develop perception algorithms for fisheye camera. One of the main problems being the lack of publicly available omnidirectional datasets in relation to drone flight. With this paper, we address this gap by presenting AirFisheye dataset, which is applicable for tasks such as segmentation, depth estimation and depth completion, among other tasks required for autonomous drone navigation. Also, a generic framework for creating synthetic fisheye images is provided. Furthermore, we propose a novel occlusion correction algorithm that removes incorrectly projected LiDAR point clouds into the camera image due to the viewpoint variation of both sensors. We release about 26K images and LiDAR scans along with annotations. Baseline code and supporting scripts are available at https://collaborating.tuhh.de/ilt/airfisheye-datasetPublicationtype: Conference Paper232