To track the pose of local optical scanners (LOSs) under occlusion and reflection, a robust speckle-textured spherical target and corresponding pose estimation methods are developed in this work, applicable to systems, such as structured light. The proposed method comprises four main stages: first, rigidly attaching the spherical target to a LOS, followed by capturing binocular images and calculating the position of the target; second, generating two panoramic images of the spherical target surface. Third, determining the target's attitude using correlation coefficient analysis matching to panoramic images; and fourth, comprehensive pose determination of the target. To validate the effectiveness of the proposed method under different occlusion and local reflection conditions, the spherical target is rigidly connected to a high-precision six-degree-of-freedom robotic arm in this work. The robotic arm serves a dual purpose: acting as a LOS whose pose is tracked and providing ground truth pose data for accuracy validation. The experimental results demonstrate that the proposed method achieves comparable accuracy under 15% occlusion conditions to state-of-the-art methods under occlusion-free scenarios, providing a valuable reference for existing target-based pose measurement techniques.

This paper introduces a novel multi-sensor fusion SLAM algorithm named FLARE-SLAM, designed for mobile robots operating in complex environments. This algorithm addresses challenges associated with uneven LiDAR measurement signals and their random distribution. First, we enhance the stability of feature extraction by refining the curvature calculation strategy for LiDAR point clouds and incorporating contextual information from the sensor array. Second, we introduce an adaptive residual optimization weight distribution mechanism, grounded in the principle of uniform residual optimization, to boost the algorithm’s adaptability across various environments. Extensive evaluations on the KITTI dataset confirm that FLARE-SLAM constructs a global map with enhanced consistency and accuracy, achieving an absolute trajectory error of 0.53% and an absolute rotation error of 0.19∘/100m. Additionally, we validate the robustness of the algorithm through real-world testing in diverse outdoor and indoor settings.

Most current light detection and ranging (LiDAR)-based simultaneous localization and mapping (SLAM) methods are based on static conditions, but real-world scenarios often violate this prior assumption. To address the existing challenges, this article proposes DO-Removal, an online LiDAR-inertial odometry that removes dynamic objects. Specifically, the method uses ground fitting results as a reference, takes point cloud measurements with significant geometric features as seed points for region growing, and uses clustering results to determine the confidence of dynamic element point cloud segmentation, thereby separating dynamic and static elements. Additionally, this article proposes a multiline LiDAR point cloud feature extraction method that considers context beams simultaneously, enhancing the significance of the extraction results. It also implements a residual optimization function based on distance truncation, distinguishing contributions by confidence, and adaptively weighting features at different distances. Finally, extensive testing was conducted on the KITTI dataset and a self-collected dataset, achieving competitive results with absolute trajectory error and absolute rotation error reduced to 0.51% and 0.19°/100 m, respectively.

In the task of semantic understanding of remote sensing images, most current research focuses on learning contextual information through attention mechanisms or multiple inductive biases. However, these methods are limited in capturing fine-grained differences within the same attribute, are susceptible to background noise interference, and lack effective modeling capabilities for spatial relationships and long-range dependencies between different remote sensing attributes. To address these issues, we specifically focus on the homogeneous and heterogeneous differences between attributes in remote sensing images. Thus, we propose an innovative comprehensive attribute difference attention network (CADANet) to enhance the performance of understanding remote sensing images. Specifically, we design two key modules: the attribute feature aggregation (AFA) module and the context attribute-aware spatial attention (CAASA) module. The AFA module primarily focuses on global and local domain attribute modeling, reducing the impact of homogeneous attribute differences through fine-grained feature extraction and global context information. The CAASA module integrates pixel-level global background information and relative position priors, employing a self-attention mechanism to capture long-range dependencies, thus addressing heterogeneous attribute differences. Extensive experimental results conducted on the widely used Vaihingen, Potsdam, and WHDLD datasets effectively demonstrate that our proposed method outperforms other recent approaches in performance. Our code is available athttps://github.com/lzp-lkd/CADANet.

In this article, we proposed a novel binocular positioning method based on a dynamic optical center imaging model to improve the accuracy of binocular positioning. By analyzing the distribution rules of the optical center at various object distances, we construct a new optical imaging model that is better suited for practical binocular positioning tasks. In addition, we develop a corresponding calibration method to accurately determine the model parameters. The experimental results demonstrate that our binocular positioning method outperforms existing methods in terms of spatial positioning and 3-D reconstruction accuracy. Compared to the binocular positioning method based on the traditional pinhole imaging model, our method achieves an 89.8% enhancement in spatial positioning accuracy and a 96.1% improvement in 3-D reconstruction accuracy for target objects. These results present the effectiveness and superiority of our method in binocular positioning applications.

Accurately freespace detection is crucial to ensure the safe operation of autonomous vehicles. However, creating multi-scene datasets can be challenging. Mainstream research primarily addresses driving scenes in urban settings while neglecting other types of road environments. This results in a constrained application environment for current freespace detection methods. This paper proposes an adaptive environment scale freespace detection method in 2D image space. The method does not require data labeling and has better environmental adaptability. The core idea is to adaptively map a fixed point cloud scale in 3D space to a pixel scale in 2D space using the camera projection relation to obtain the fine environmental gradient. Then design search rule to label freespace in 2D space. Experiments on two public datasets, urban and field, achieved F1 scores of 92.50% and 89.09%, respectively. In both structured and unstructured environments, the proposed method demonstrated higher accuracy and lower false detection rates compared to state-of-the-art methods.

The process of recovering dense depth maps from sparse depth information is prone to edge blurring. This paper proposes an image-guided depth completion algorithm to address this issue. The method uses the edges of the color image as prior constraints to construct an image topography map as an intermediate representation and performs nonlinear adaptive reconstruction based on the image content to adjust the position and scale of the pixel-weighted neighborhood. This approach avoids incorporating depth information with different distributions when estimating missing values, resulting in a full-resolution dense depth map with sharp edges. We conducted a quantitative comparison with state-of-the-art models on the KITTI and MidAir datasets, demonstrating that our algorithm has better performance and robustness in terms of completion accuracy. We also analyzed the impact of sparsity on the algorithm’s performance and its ability to recover fine structures in dense depth results and demonstrated the reconstruction results for sparse data in real-world scenarios.

The multiattribute scene understanding (MASU) tasks currently lie in capturing multiple attribute features and learning the complex correlations between different attributes. Traditional methods primarily focus on exploring multiscale local insights and employ direct approaches to merge global semantic data into the image models, thereby neglecting the full spectrum of global semantic features across different receptive fields. Furthermore, encapsulating a wide range of spatial details through deeper networks inevitably leads to a drastic increase in computational complexity. To address these challenges, we propose a novel end-to-end network named adjacent-atrous mechanism for expanding global receptive fields (AMEGRF-Net). Specifically, we introduce an efficient local-global feature learning paradigm that innovatively expands the model’s receptive field to enhance scene understanding without incurring additional computational overhead. A local feature sensing (LFS) module is proposed to enhance the distinctiveness between different categories within the feature space while refining the spatial feature learning capability and interchannel synergy. We present an innovative adjacent-atrous mechanism, adjacent-atrous global context modeling module (AGCM), to combine a broader global receptive field with a complex relationship capturing mechanism, achieving deep modeling of the intricate relationships between attributes and labels. Through extensive comparative experiments on three challenging public datasets, the superior performance of AEGRF-Net in handling high-resolution remote sensing images for MASU has been clearly demonstrated.

Due to the divergence of accuracy caused by inertial measurement unit (IMU) cumulative error, it is difficult for a single IMU equipment to realize vehicle positioning. Therefore, this paper proposes an IMU pose state estimation algorithm based on modulation long short-term memory-unscented Kalman filter (ML-UKF) algorithm. First, the algorithm improves the memory mode of LSTM network by using Modulation LSTM neural network and establishes IMU state model and observation model. Then, in order to adapt to the application of deep learning algorithm in UKF, an equal spacing sigma sampling method is proposed. Finally, the effect of IMU pose state estimation is verified by experiments. Results show that the root mean square error of the ML-UKF algorithm is decreases by 65.43% relative to the state of the art, further verifying the effectiveness of the proposed algorithm.

MOTIVATION: Whole-genome sequencing (WGS) is increasingly used to aid the understanding of Mycobacterium tuberculosis (MTB) transmission. The epidemiological analysis of tuberculosis based on the WGS technique requires a diverse collection of bioinformatics tools. Effectively using these analysis tools in a scalable and reproducible way can be challenging, especially for non-experts. RESULTS: Here, we present TransFlow (Transmission Workflow), a user-friendly, fast, efficient and comprehensive WGS-based transmission analysis pipeline. TransFlow combines some state-of-the-art tools to take transmission analysis from raw sequencing data, through quality control, sequence alignment and variant calling, into downstream transmission clustering, transmission network reconstruction and transmission risk factor inference, together with summary statistics and data visualization in a summary report. TransFlow relies on Snakemake and Conda to resolve dependencies among consecutive processing steps and can be easily adapted to any computation environment. AVAILABILITY AND IMPLEMENTATION: TransFlow is free available at https://github.com/cvn001/transflow. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.