Integrated design-sense-plan architecture for autonomous geometric-semantic mapping with UAVs.

This article presents a complete solution for autonomous mapping and inspection tasks, namely a lightweight multi-camera drone design coupled with computationally efficient planning algorithms and environment representations for enhanced autonomous navigation in exploration and mapping tasks. The proposed system utilizes state-of-the-art Next-Best-View (NBV) planning techniques, with geometric and semantic segmentation information computed with Deep Convolutional Neural Networks (DCNNs) to improve the environment map representation. The main contributions of this article are the following. First, we propose a novel efficient sensor observation model and a utility function that encodes the expected information gains from observations taken from specific viewpoints. Second, we propose a reward function that incorporates both geometric and semantic probabilistic information provided by a DCNN for semantic segmentation that operates in close to real-time. The incorporation of semantics in the environment representation enables biasing exploration towards specific object categories while disregarding task-irrelevant ones during path planning. Experiments in both a virtual and a real scenario demonstrate the benefits on reconstruction accuracy of using semantics for biasing exploration towards task-relevant objects, when compared with purely geometric state-of-the-art methods. Finally, we present a unified approach for the selection of the number of cameras on a UAV, to optimize the balance between power consumption, flight-time duration, and exploration and mapping performance trade-offs. Unlike previous design optimization approaches, our method is couples with the sense and plan algorithms. The proposed system and general formulations can be be applied in the mapping, exploration, and inspection of any type of environment, as long as environment dependent semantic training data are available, with demonstrated successful applicability in the inspection of dry dock shipyard environments.

Correction: Tøttrup et al. A Real-Time Method for Time-to-Collision Estimation from Aerial Images. J. Imaging 2022, 8, 62.

The authors wish to make the following corrections to the article [...].

A Real-Time Method for Time-to-Collision Estimation from Aerial Images.

Large vessels such as container ships rely on experienced pilots with extensive knowledge of the local streams and tides responsible for operating the vessel to its desired location. This work proposes estimating time-to-collision (TTC) between moving objects (i.e., vessels) using real-time video data captured from aerial drones in dynamic maritime environments. Our deep-learning-based methods utilize features optimized with realistic virtually generated data for reliable and robust object detection, segmentation, and tracking. Furthermore, we use rotated bounding box representations, obtained from fine semantic segmentation of objects, for enhanced TTC estimation accuracy. We intuitively present collision estimates as collision arrows that gradually change color to red to indicate an imminent collision. Experiments conducted in a realistic dockyard virtual environment show that our approaches precisely, robustly, and efficiently predict TTC between dynamic objects seen from a top-view, with a mean error and a standard deviation of 0.358 0.114 s, respectively, in a worst-case scenario.

A Fast and Accurate Approach to Multiple-Vehicle Localization and Tracking from Monocular Aerial Images.

In this work we present a novel end-to-end solution for tracking objects (i.e., vessels), using video streams from aerial drones, in dynamic maritime environments. Our method relies on deep features, which are learned using realistic simulation data, for robust object detection, segmentation and tracking. Furthermore, we propose the use of rotated bounding-box representations, which are computed by taking advantage of pixel-level object segmentation, for improved tracking accuracy, by reducing erroneous data associations during tracking, when combined with the appearance-based features. A thorough set of experiments and results obtained in a realistic shipyard simulation environment, demonstrate that our method can accurately, and fast detect and track dynamic objects seen from a top-view.