Visual Place Recognition (VPR) plays a vital role in mobile robotics and autonomous navigation by retrieving reference images from a pre-established database. However, VPR systems frequently encounter performance degradation due to environmental variations. To overcome these challenges, we propose a re-ranking based VPR framework incorporating two key components: (1) A Visual Mamba Embedding (VME) module that optimizes spatial-channel feature interactions to generate discriminative global descriptors; and (2) A Spatial Graph Attentional Network (SGAN) that replaces conventional RANSAC-based verification with an efficient graph attention mechanism, improving matching accuracy while reducing computation. Comprehensive evaluations across multiple benchmark datasets demonstrate that the proposed method achieves superior performance compared to existing state-of-the-art methods, while maintaining advantages in computational efficiency and storage requirements.

Against the backdrop of an aging population and the high prevalence of chronic diseases, the demand for rehabilitation medical services has surged. However, traditional rehabilitation action difficulty assessment relies on expert experience, suffering from strong subjectivity and low reliability. Existing assessment methods struggle to cover the full-body kinematic characteristics, and existing models lack directed modeling of action difficulty relationships and fail to effectively capture muscle synergy. To address this, this study constructs a 16-channel full-body sEMG dataset, sEmgHuman-594, which includes 594 rehabilitation actions. This study proposes an sEMG-DGCN assessment method based on Directed Graph Convolutional Network (DGCN), which integrates 11-dimensional expert-annotated difficulty criteria to derive difficulty labels, employs directed graphs to model difficulty relationships between actions, and introduces an Anatomically Constrained Spatiotemporal Attention mechanism. Experimental results show that the sEMG-DGCN model achieves an accuracy of 93.61% in difficulty relationship classification, significantly outperforming comparative models. Ablation experiments verify the effectiveness of the attention mechanism, providing a new pathway for rehabilitation action difficulty assessment.

Accurate localization of autonomous vehicles is crucial for autonomous driving and safety, especially in complex urban environments where high-precision GPS is not available. Visual place recognition (VPR) uses visual cues to identify the current location from a known database, serving as an auxiliary means for precise localization in autonomous driving. In real-world applications, variations in scene appearance due to changes in illumination and seasons present significant challenges for VPR. Current VPR methods often fail in adverse visual environments due to their inability to provide robust scene descriptions. Therefore, the extraction of stable and effective information from images is relatively important. In this paper, we propose a novel feature extraction network, termed SBCNet. This network is designed to capture semantic boundaries and texture details within images by training an auxiliary semantic boundary detection task. By focusing on these fundamental elements, the model’s perceptual capacity for structural features can be enhanced. Moreover, we introduce a semantic edge attention module that generates spatial attention maps based on semantic edges and texture details, allowing for the comprehensive utilization of pivotal structural cues. With this explicit guide, the network prioritizes local regions with appearance invariance during the feature extraction process. Experimental results demonstrate that our method maintains robust performance under various adverse visual conditions. Even in low-light environments, such as those encountered at night, our method exhibits commendable performance.

Accurate and robust LiDAR odometry is a crucial technology for robot localization. However, motion distortion and ranging error make it a bottleneck. Most existing methods are limited in accuracy and robustness because they simply compensate for motion distortion by constant velocity motion assumption without accurate model of ranging error. In this paper, we propose a high-precision and robust LiDAR odometry (LO), which utilizes continuous-time estimation to remove LiDAR distortion and builds the spot uncertainty model to quantify the ranging error. Generally, the number of variables in continuous-time estimation is several times higher than that in discrete-time ones, leading to insufficient constraints on the LiDAR odometry. To solve this problem, we propose a marginalization method to retain prior scans’ constraints by exploiting the local support property of the B-spline. To further improve the odometry accuracy, we propose a residual adaptive weighting method and a probabilistic point cloud map based on the spot uncertainty model of LiDAR points. The experimental results show that our method outperforms state-of-the-art LiDAR odometry in accuracy and robustness.

Visual place recognition(VPR) is a crucial technology for autonomous driving and robotic navigation. However, severe appearance and perspective changes often lead to degradation of algorithm performance. Current methods mainly utilize single-modality RGB images, which are sensitive to environmental changes. To address this challenge, we propose a novel multi-modal visual place recognition method by incorporating depth information as auxiliary data to enhance the robustness of the VPR algorithm. The pipeline involves dual-branch feature extraction and shared multi-modal feature fusion based on transformer(SFFM) to enable full interaction between semantic and structural information. Furthermore, we introduces a time-constrained graph attention aggregation(TC-GAT) that propagates node information across time and space to deal with perceptual aliasing. Extensive experiments on the Oxford Robotcar and MSLS datasets demonstrate that the proposed algorithm is not only effective in appearance changes but also competitive in opposing viewpoints.

As a crucial part of mobile robotics and autonomous driving, Visual Place Recognition (VPR) is usually addressed by recognizing its similar reference images from a pre-obtained database. However, VPR always suffers from environmental changes, such as weather, illumination, perceptual-aliasing and so on. To address this, we firstly introduce a robust and discriminative global descriptor aggregation technique that normalizes the spatial and channel dimensions of features. A Spatial-Channel Embedding (SCE) module is proposed to learn the spatial and scale information of features which make global features more discriminative. Meanwhile, the traditional re-ranking methods (e.g. RANSAC) for geometric consistency verification are time-consuming. Here we propose a Neighborhood Consensus Guided Matching (NCGM) module, which uses Neighborhood Consensus to filter the features from patch-level matching to achieve more accurate matching while reduces the time consumption. Through extensive experiments on multiple benchmarks, we demonstrate that our method outperforms several state-of-the-art methods while maintaining lower time consumption and storage requirements.

Real-time and robust long-term visual localization is a crucial technology for autonomous driving. Season and illumination variance make this problem more challenging. At present, most of excellent visual localization algorithms cannot run in real-time on devices with limited computing resources. In this paper, we propose SAMLoc, a structure-aware and self-supervised visual localization system, for fast and robust 6-DoF localization. To obtain structural features in the scene, we propose local and global structure-aware constraints using edge information. Then, we integrate the structure-aware constraints into the hierarchical localization network of multi-task distillation, which significantly reduces the feature extraction time while ensuring localization accuracy. As a result, real-time and robust large-scale localization can be achieved on mobile devices. Experimental results on public datasets show that our system can achieve high localization accuracy and have satisfactory real-time performance. Compared with several state-of-the-art visual localization systems, our framework achieves a competitive localization performance.