Most existing dynamic SLAM methods integrating neural networks require high computational support and pre-defined categories of dynamic objects. This paper proposes an online visual SLAM system without pre-defined dynamic labels for dynamic environments, which is built on ORB-SLAM3. We combine semantic information, depth information and optical flow to distinguish foreground and background and recognize dynamic regions. The foreground points in dynamic areas are regarded as moving points. We also restore static points on moving objects by the average re-projection error between multiple frames to adapt to non-rigid motion and low-dynamic environments. In pose optimization, we define an optimization weight for every point to decrease the negative influence of potential dynamic points. The experiments on TUM RGB-D and Bonn datasets show that OVD-SLAM achieves more accurate and robust localization than other state-of-art dynamic SLAM methods. To further verify our system’s online performance and robustness, we also test it in real dynamic scenes and apply it to an AR application using a handheld RGB-D camera.

Ship target detection in synthetic aperture radar (SAR) images is essential for many applications in marine monitoring and port security. Though considerable developments have been achieved, there still exist some issues toward multiscale and dense ship targets in complex inshore scenes. Under such common but challenging situations, it is difficult to extract effective target information, which drives the missing alarm rate rising dramatically. In complex scenes, it is hard to disentangle background noise from ship target information, which causes false alarm frequently. In this article, an anchor-free SAR ship detection method based on key-point estimation and attention mechanism is proposed to address the aforementioned issues. Specially, an anchor-free framework with skip connections and aggregation nodes is designed to fuse multiresolution features and detect multiscale ship targets. Moreover, a key-point estimation module is proposed to eliminate the undetected ship targets caused by dense target distribution. Furthermore, a channel attention module is explored to enhance network attention on ship targets and suppress background noise. Sufficient experimental results on the open SAR ship detection dataset demonstrate that compared with some state-of-the-art methods, the proposed method is able to achieve higher detection accuracy with a lower false alarm rate.

The visual loop-closure detection for autonomous underwater vehicles (AUVs) is a key component to reduce the drift error accumulated in simultaneous localization and mapping tasks. However, due to viewpoint changes, textureless images, and fast-moving objects, the loop closure detection in dramatically changing underwater environments remains a challenging problem to traditional geometric methods. Inspired by strong feature learning ability of deep neural networks, we propose an underwater loop-closure detection method based on a variational autoencoder network in this article. Our proposed method can learn effective image representations to deal with the challenges caused by dynamic underwater environments. Specifically, the proposed network is an unsupervised method, which avoids the difficulty and cost of labeling a great quantity of underwater data. Also included is a semantic object segmentation module, which is utilized to segment the underwater environments and assign weights to objects in order to alleviate the impact of fast-moving objects. Furthermore, an underwater image description scheme is used to enable efficient access to geometric and object-level semantic information, which helps to build a robust and real-time system in dramatically changing underwater scenarios. Finally, we test the proposed system under complex underwater environments and get a recall rate of 92.31% in the tested environments.

Autonomous underwater vehicles (AUVs) play an important role in subaqueous construction and oceanographic survey, in which AUV self-localization is a crucial component. Current localization methods mainly depend on expensive acoustic positioning systems, which limit the wide application of AUVs. In this paper, referring to the low-cost visual localization method and focusing on challenges caused by underwater environment, we propose an underwater self-localization method based on Pseudo-3D vision-inertia for AUVs. The proposed method merges depth information into 2D visual image to achieve continuous and robust localization under dramatically changing underwater environment. In order to decrease errors, we propose an online fusion method based on tightly-coupled nonlinear optimization to fuse the measurements of the pre-integrated inertial measurement unit and the observations from the down-looking camera. We also optimize four degrees-of-freedom pose graph to enhance the global consistency and design an online loop detection module to realize the underwater relocalization. In addition, we develop a low-cost, portable, and small volume sensor suite for underwater vehicle localization and test the proposed self-localization method. We test the proposed method in the underwater environment using the custom-made sensor suite, and the experimental results demonstrate the effectiveness of the proposed method under dramatically changing underwater environment.

Autonomous underwater vehicles (AUVs) play an important role in deep-sea exploration, in which AUV self-localization is a key component. However, due to poor visibility caused by challenging marine environments, AUVs are often equipped with high-cost and heavy-weight acoustic sensors to accomplish localization tasks. We propose a robust real-time AUV self-localization method based on stereo camera and inertial sensor, which merges point and diagonal features, as well as inertial measurements to overcome the challenges of poor visibility. Our method also includes an underwater loop detection algorithm based on the combination of points and diagonal segments, which can extract effective binary descriptors in low-textured underwater scenarios. Furthermore, we develop an AUV self-localization system based on a real-time, portable, low-cost, and small volume sensor suite. Finally, we test the proposed method in a real underwater environment using our sensor suite, and the experimental results demonstrate the effectiveness of the proposed method under dramatically changing underwater scenarios.