Cloud detection and cloud properties have substantial applications in weather forecast, signal attenuation analysis, and other cloud-related fields. Cloud image segmentation is the fundamental and important step in deriving cloud cover. However, traditional segmentation methods rely on low-level visual features of clouds and often fail to achieve satisfactory performance. Deep convolutional neural networks (CNNs) can extract high-level feature information of objects and have achieved remarkable success in many image segmentation fields. On this basis, a novel deep CNN model named SegCloud is proposed and applied for accurate cloud segmentation based on ground-based observation. Architecturally, SegCloud possesses a symmetric encoder–decoder structure. The encoder network combines low-level cloud features to form high-level, low-resolution cloud feature maps, whereas the decoder network restores the obtained high-level cloud feature maps to the same resolution of input images. The Softmax classifier finally achieves pixel-wise classification and outputs segmentation results. SegCloud has powerful cloud discrimination capability and can automatically segment whole-sky images obtained by a ground-based all-sky-view camera. The performance of SegCloud is validated by extensive experiments, which show that SegCloud is effective and accurate for ground-based cloud segmentation and achieves better results than traditional methods do. The accuracy and practicability of SegCloud are further proven by applying it to cloud cover estimation.

State estimation is crucial for robot autonomy, visual odometry (VO) has received significant attention in the robotics field because it can provide accurate state estimation. However, the accuracy and robustness of most existing VO methods are degraded in complex conditions, due to the limited field of view (FOV) of the utilized camera. In this paper, we present a novel tightly-coupled multi-keyframe visual-inertial odometry (called VINS-MKF), which can provide an accurate and robust state estimation for robots in an indoor environment. We first modify the monocular ORBSLAM (Oriented FAST and Rotated BRIEF Simultaneous Localization and Mapping) to multiple fisheye cameras alongside an inertial measurement unit (IMU) to provide large FOV visual-inertial information. Then, a novel VO framework is proposed to ensure the efficiency of state estimation, by adopting a GPU (Graphics Processing Unit) based feature extraction method and parallelizing the feature extraction thread that is separated from the tracking thread with the mapping thread. Finally, a nonlinear optimization method is formulated for accurate state estimation, which is characterized as being multi-keyframe, tightly-coupled and visual-inertial. In addition, accurate initialization and a novel MultiCol-IMU camera model are coupled to further improve the performance of VINS-MKF. To the best of our knowledge, it’s the first tightly-coupled multi-keyframe visual-inertial odometry that joins measurements from multiple fisheye cameras and IMU. The performance of the VINS-MKF was validated by extensive experiments using home-made datasets, and it showed improved accuracy and robustness over the state-of-art VINS-Mono.

Cloud detection and cloud properties have significant applications in weather forecast, signal attenuation analysis, and other cloud-related fields. Cloud image segmentation is the fundamental and important step to derive cloud cover. However, traditional segmentation methods rely on low-level visual features of clouds, and often fail to achieve satisfactory performance. Deep Convolutional Neural Networks (CNNs) are able to extract high-level feature information of object and have become the dominant methods in many image segmentation fields. Inspired by that, a novel deep CNN model named SegCloud is proposed and applied to accurate cloud segmentation based on ground-based observation. Architecturally, SegCloud possesses symmetric encoder-decoder structure. The encoder network combines low-level cloud features to form high-level cloud feature maps with low resolution, and the decoder network restores the obtained high-level cloud feature maps to the same resolution of input images. The softmax classifier finally achieves pixel-wise classification and outputs segmentation results. SegCloud has powerful cloud discrimination ability and can automatically segment the whole sky images obtained by a ground-based all-sky-view camera. Furthermore, a new database, which includes 400 whole sky images and manual-marked labels, is built to train and test the SegCloud model. The performance of SegCloud is validated by extensive experiments, which show that SegCloud is effective and accurate for ground-based cloud segmentation and achieves better results than traditional methods. Moreover, the accuracy and practicability of SegCloud is further proved by applying it to cloud cover estimation.