Accurate bow direction detection is essential for ship trajectory prediction and port monitoring. Existing ship detection networks typically output angles within 180°, while extending to 360° introduces cyclic issues affecting rotation intersection over union (RIoU) accuracy. This study proposes a novel bow direction detection algorithm that extends network output to 360° and integrates a heading intersection over union (HIoU) loss to enhance detection accuracy and robustness. Additionally, an HIoU loss function is designed to improve bow direction identification and reduce quantization errors in hash codes. The algorithm is evaluated on three datasets: FGSD, OHD-SJTU-S, and OHD-SJTU-L. On FGSD, it achieves mean average precision (mAP) of 91.14%. On OHD-SJTU-S, it attains an$\text {mAP}_{50:95}$of 63.3% and a bow direction prediction accuracy of 90.7%. On OHD-SJTU-L, the$\text {mAP}_{50:95}$is 29.2%, with an accuracy of 80.2%.

Existing one-stage object detectors are commonly implemented in a multi-task learning based manner, which simultaneously solves two different sub-tasks: object classification and localization. To achieve this, the detection heads with two independent branches are typically utilized to extract specific image features for each task separately. However, due to the lack of interaction between the parallel branches, the difference in learning objectives of classification and localization will lead to spatial misalignment between the predictions of these two tasks. In this work, we propose a novel Cross-attentive Task-aligned Object Detection (CTOD) method to handle this problem by explicitly promoting the prediction consistency for both tasks. Specifically, we first design a Dual Task Interaction (DTI) module, which generates task-interactive embeddings for each branch from task-specific features by using a task cross-attention mechanism. Then based on these embeddings, we propose a Spatial Feature Aggregation (SFA) module that calculates offsets and weights to aggregate information from nearby feature points at each spatial location of the task-specific feature maps. Meanwhile, we also generate adjustment parameters from the task-interactive embeddings to finally align the prediction results of the two tasks obtained from the enhanced task-specific features described above. Extensive experiments are conducted on the MS-COCO dataset. When using ResNeXt-101-$64\times 4$d-DCN as the backbone, our CTOD method achieves a detection result of 51.8 AP with single-model and single-scale testing, outperforming the recently proposed one-stage detectors ATSS, VFNet, LD and TOOD by 4.1, 1.9, 1.3 and 0.7 AP, respectively. The analysis of qualitative results also illustrates the effectiveness and superiority of CTOD in solving the task misalignment problem for object detection. Our code is available athttps://github.com/Mr-Bigworth/CTOD.

Recently, oriented object detection in remote sensing images has garnered significant attention due to its broad range of applications. Early oriented object detection adhered to the established general object detection frameworks, utilizing the label assignment strategy based on the horizontal bounding box annotations or rotation-agnostic cost function. Such strategy may not reflect the large aspect ratio and rotation of arbitrary-oriented objects in remota sensing images and require high parameter-tuning efforts in training process, which will eventually harm the detector performance. Furthermore, the localization quality of oriented object depends on precise rotation angle prediction, exacerbating the inconsistency between classification and regression tasks in oriented object detection. To address these issues, we propose the Gaussian Distribution Cost Optimal Transport Assignment (GCOTA) and Decoupled Layer Attention Angle Head (DLAAH). Specifically, GCOTA utilize Gaussian distribution based cost function for the optimal transport label assignment in training process, alleviating the impact of rotation angle and large aspect ratio in remote sensing images. DLAAH predicts rotation angle independently and incorporates layer attention to obtain the task-specific features based on the shared FPN features, enhancing the angle prediction and improving consistency across different tasks. Based on these proposed components, we present an anchor-free oriented detector, namely Gaussian Distribution and Task-Decoupled head oriented Detector(GTDet) and a a multi-class ship detection dataset in real scenarios (CGWX), which provides a benchmark for fine-grained object recognition in remote sensing images. Comprehensive experiments are conducted on CGWX and several public challenging datasets, including DOTAv1.0, HRSC2016, to demonstrate that our method achieves superior performance on oriented object detection task. The code is available at https://github.com/WUTCM-Lab/GTDet.

Inferring the posterior distribution in SLAM is critical for evaluating the uncertainty in localization and mapping, as well as supporting subsequent planning tasks aiming to reduce uncertainty for safe navigation. However, real-time full posterior inference techniques, such as Gaussian approximation and particle filters, either lack expressiveness for representing non-Gaussian posteriors or suffer from performance degeneracy when estimating high-dimensional posteriors. Inspired by the complementary strengths of Gaussian approximation and particle filters-scalability and non-Gaussian estimation, respectively-we blend these two approaches to infer marginal posteriors in SLAM. Specifically, Gaussian approximation provides robot pose distributions on which particle filters are conditioned to sample landmark marginals. In return, the maximum a posteriori point among these samples can be used to reset linearization points in the nonlinear optimization solver of the Gaussian approximation, facilitating the pursuit of global optima. We demonstrate the scalability, generalizability, and accuracy of our algorithm for real-time full posterior inference on realworld range-only SLAM and object-based bearing-only SLAM datasets.

This article presents normalizing flows for incremental smoothing and mapping (NF-iSAM), a novel algorithm for inferring thefullposterior distribution in SLAM problems with nonlinear measurement models and non-Gaussian factors. NF-iSAM exploits the expressive power of neural networks, and trains normalizing flows to model and sample the full posterior. By leveraging the Bayes tree, NF-iSAM enables efficient incremental updates similar to iSAM2, albeit in the more challengingnon-Gaussiansetting. We demonstrate the advantages of NF-iSAM over state-of-the-art point and distribution estimation algorithms using range-only SLAM problems with data association ambiguity. NF-iSAM presents superior accuracy in describing the posterior beliefs of continuous variables (e.g., position) and discrete variables (e.g., data association).

This paper presents a novel non-Gaussian inference algorithm, Normalizing Flow iSAM (NF-iSAM), for solving SLAM problems with non-Gaussian factors and/or non-linear measurement models. NF-iSAM exploits the expressive power of neural networks, and trains normalizing flows to draw samples from the joint posterior of non-Gaussian factor graphs. By leveraging the Bayes tree, NF-iSAM is able to exploit the sparsity structure of SLAM, thus enabling efficient incremental updates similar to iSAM2, albeit in the more challenging non- Gaussian setting. We demonstrate the performance of NF-iSAM and compare it against the state-of-the-art algorithms such as iSAM2 (Gaussian) and mm-iSAM (non-Gaussian) in synthetic and real range-only SLAM datasets.

In object-based Simultaneous Localization and Mapping (SLAM), 6D object poses offer a compact representation of landmark geometry useful for downstream planning and manipulation tasks. However, measurement ambiguity then arises as objects may possess complete or partial object shape symmetries (e.g., due to occlusion), making it difficult or impossible to generate a single consistent object pose estimate. One idea is to generate multiple pose candidates to counteract measurement ambiguity. In this paper, we develop a novel approach that enables an object-based SLAM system to reason about multiple pose hypotheses for an object, and synthesize this locally ambiguous information into a globally consistent robot and landmark pose estimation formulation. In particular, we (1) present a learned pose estimation network that provides multiple hypotheses about the 6D pose of an object; (2) by treating the output of our network as components of a mixture model, we incorporate pose predictions into a SLAM system, which, over successive observations, recovers a globally consistent set of robot and object (landmark) pose estimates. We evaluate our approach on the popular YCB-Video Dataset and a simulated video featuring YCB objects. Experiments demonstrate that our approach is effective in improving the robustness of object-based SLAM in the face of object pose ambiguity.1

Building object-level maps can facilitate robot-environment interactions (e.g. planning and manipulation), but objects could often have multiple probable poses when viewed from a single vantage point, due to symmetry, occlusion or perceptual failures. A robust object-level simultaneous localization and mapping (object SLAM) algorithm needs to be aware of this pose ambiguity. We propose to maintain and subsequently disambiguate the multiple pose interpretations to gradually recover a globally consistent world representation. The max-mixtures model is applied to implicitly and efficiently track all pose hypotheses, but the resulting formulation is non-convex, and therefore subject to local optima. To mitigate this problem, temporally consistent hypotheses are extracted, guiding the optimization into the global optimum. This consensus-informed inference method is applied online via landmark variable re-initialization within an incremental SLAM framework, iSAM2, for robust real-time performance. We demonstrate that this approach improves SLAM performance on both simulated and real object SLAM problems with pose ambiguity.