Magnetic-Assisted Initialization for Infrastructure-free Mobile Robot Localization

Most of the existing mobile robot localization solutions are either heavily dependent on pre-installed infrastructures or having difficulty working in highly repetitive environments which do not have sufficient unique features. To address this problem, we propose a magnetic-assisted initialization approach that enhances the performance of infrastructure-free mobile robot localization in repetitive featureless environments. The proposed system adopts a coarse-to-fine structure, which mainly consists of two parts: magnetic field-based matching and laser scan matching. Firstly, the interpolated magnetic field map is built and the initial pose of the mobile robot is partly determined by the k-Nearest Neighbors (k-NN) algorithm. Next, with the fusion of prior initial pose information, the robot is localized by laser scan matching more accurately and efficiently. In our experiment, the mobile robot was successfully localized in a featureless rectangular corridor with a success rate of 88% and an average correct localization time of 6.6 seconds

The Snowflake Hypothesis: Training Deep GNN with One Node One Receptive field

Despite Graph Neural Networks demonstrating considerable promise in graph representation learning tasks, GNNs predominantly face significant issues with over-fitting and over-smoothing as they go deeper as models of computer vision realm. In this work, we conduct a systematic study of deeper GNN research trajectories. Our findings indicate that the current success of deep GNNs primarily stems from (I) the adoption of innovations from CNNs, such as residual/skip connections, or (II) the tailor-made aggregation algorithms like DropEdge. However, these algorithms often lack intrinsic interpretability and indiscriminately treat all nodes within a given layer in a similar manner, thereby failing to capture the nuanced differences among various nodes. To this end, we introduce the Snowflake Hypothesis -- a novel paradigm underpinning the concept of ``one node, one receptive field''. The hypothesis draws inspiration from the unique and individualistic patterns of each snowflake, proposing a corresponding uniqueness in the receptive fields of nodes in the GNNs. We employ the simplest gradient and node-level cosine distance as guiding principles to regulate the aggregation depth for each node, and conduct comprehensive experiments including: (1) different training schemes; (2) various shallow and deep GNN backbones, and (3) various numbers of layers (8, 16, 32, 64) on multiple benchmarks (six graphs including dense graphs with millions of nodes); (4) compare with different aggregation strategies. The observational results demonstrate that our hypothesis can serve as a universal operator for a range of tasks, and it displays tremendous potential on deep GNNs. It can be applied to various GNN frameworks, enhancing its effectiveness when operating in-depth, and guiding the selection of the optimal network depth in an explainable and generalizable way

NTU4DRadLM: 4D Radar-centric Multi-Modal Dataset for Localization and Mapping

Simultaneous Localization and Mapping (SLAM) is moving towards a robust perception age. However, LiDAR- and visual- SLAM may easily fail in adverse conditions (rain, snow, smoke and fog, etc.). In comparison, SLAM based on 4D Radar, thermal camera and IMU can work robustly. But only a few literature can be found. A major reason is the lack of related datasets, which seriously hinders the research. Even though some datasets are proposed based on 4D radar in past four years, they are mainly designed for object detection, rather than SLAM. Furthermore, they normally do not include thermal camera. Therefore, in this paper, NTU4DRadLM is presented to meet this requirement. The main characteristics are: 1) It is the only dataset that simultaneously includes all 6 sensors: 4D radar, thermal camera, IMU, 3D LiDAR, visual camera and RTK GPS. 2) Specifically designed for SLAM tasks, which provides fine-tuned ground truth odometry and intentionally formulated loop closures. 3) Considered both low-speed robot platform and fast-speed unmanned vehicle platform. 4) Covered structured, unstructured and semi-structured environments. 5) Considered both middle- and large- scale outdoor environments, i.e., the 6 trajectories range from 246m to 6.95km. 6) Comprehensively evaluated three types of SLAM algorithms. Totally, the dataset is around 17.6km, 85mins, 50GB and it will be accessible from this link: https://github.com/junzhang2016/NTU4DRadLMComment: 2023 IEEE International Intelligent Transportation Systems Conference (ITSC 2023

Visual place recognition for unmanned vehicles in city-scale challenging environments

Large-scale visual place recognition (VPR) involves retrieving reference images that depict the same location of the given query image. It can be applied to loop closure detection within Simultaneous Locations and Mapping (SLAM) for robot systems. With the rapid development of mobile robots, long-term navigation poses greater challenges to the VPR task. For example, the same place may undergo extreme appearance changes due to different illumination, weather, and seasonal conditions. The task difficulty is also increased by partial occlusion and dynamic objects. Additionally, a robot may revisit the same place with different viewpoints. Therefore, large-scale visual place recognition under challenging conditions has raised widespread concerns in both Computer Vision and Robotics communities. To effectively handle the task, researchers have attempted to present solutions from a variety of perspectives, which can be broadly divided into two categories. The first type of methods is devoted to the development of powerful global image descriptors for fast and accurate retrieval. A nearest neighbor search on the query image descriptor will highlight candidate reference images with smaller feature space distances from the query image. The second type of methods, in addition to the global approach, refocuses on local details. They take advantage of the spatial consistency of pixels or patches to geometrically validate the candidate reference images obtained through global retrieval. In general, these two types of methods still have some inherent flaws that must be addressed. The global approaches are centered on developing compact and discriminative image descriptors. Early methods indiscriminately quantify all local features into the feature embedding, which may result in misleading information being encoded into the image representation. In order to highlight the task-relevant visual cues in the feature embedding, the existing attention mechanisms are either based on artificial rules or trained in a thorough data-driven manner. To fill the gap between the two types, a novel Semantic Reinforced Attention Learning (SRAL) model is firstly proposed, in which the inferred attention can benefit from both semantic priors and data-driven training. The contribution lies in two-fold. (1) An interpretable local weighting scheme based on hierarchical feature distribution is proposed to suppress misleading local features. (2) By exploiting the interpretability of the local weighting scheme, a semantic constrained initialization is proposed so that the local attention can be reinforced by semantic priors. On city-scale benchmark datasets, experiments show that SRALNet outperforms previous state-of-the-art (SOTA) global image descriptors for VPR. Secondly, the task relevance of visual cues is heavily influenced by their context in the scene. With this in mind, a novel encoding strategy called Attentional Pyramid Pooling of Salient Visual Residuals (APPSVR) is proposed on top of SRALNet. It incorporates three types of attention modules to model the saliency of local features in individual, spatial and cluster dimensions respectively. (1) A semantic-reinforced local weighting scheme is used for local feature refinement to inhibit task irrelevant local features; (2) To leverage the spatial context, an attentional pyramid structure is constructed to adaptively encode regional features according to their relative spatial saliency; (3) To distinguish the different importance of visual clusters to the task, a parametric normalization is proposed to adjust their contribution to image descriptor generation. Experiments demonstrate that APPSVR outperforms the existing techniques and achieves a new state-of-the-art performance on VPR benchmark datasets. The visualization shows the saliency map learned in a weakly supervised manner is generally consistent with human cognition. Thirdly, global approaches rely heavily on aggregation to produce compact image descriptors, at the expense of decoupling spatial information and ignoring local details. This may cause confusion in the retrieval of multiple scenes with similar appearances. Hopefully, a geometric consistency check of local pixels or patches will be able to validate the candidate reference images obtained by the global retrieval. According to this, a Co-Attentive Hierarchical Image Representations (CAHIR) framework is proposed for VPR, which unifies attention-sharing global and local descriptor generation into a single encoding pipeline. The hierarchical descriptors are applied to a coarse-to-fine VPR system with global retrieval and local geometric verification. To explore high-quality local matches between task-relevant visual elements, a cross-attention mutual enhancement layer is introduced to strengthen the information interaction between the local descriptors. In order for the mutual enhancement layer to perform optimally, we propose a distillation pipeline with novel selective matching loss, through which the parametric model can be fine-tuned through distillation learning. After cross-matching the enhanced local descriptors, only local correspondences with high task-relevance are preserved for subsequent geometric consistency assessment. Experiments demonstrate that CAHIR outperforms the existing global and local representations for VPR. It achieves new state-of-the-art results on city-scale benchmark datasets. The visualization also shows the learned CAHIR can place a high value on task-relevant visual elements and excels at locating local correspondences that are discriminative to the VPR task.Doctor of Philosoph

Improved ant colony optimization for solving dial-a-ride-problem

The dial-a-ride problem (DARP) is a combinatorial optimization problem in which passengers claim requests in the form of their departure location, destination and the specific time windows during which they must be picked up and dropped off. A certain number of vehicles are assigned to serve these requests while ensuring that the maximum capacities of vehicles are not exceeded and the maximum ride time constraints of passengers, if any, are not violated. In this thesis, an improved ant colony optimization (IACO) algorithm is proposed to address the dial-a-ride-problem. The proposed algorithm works by pre-processing the requests to eliminate the ones that are infeasible from the beginning. In order to select the requests, the algorithm considers factors like time windows, local heuristics and vehicle load. Also, an adjust function is introduced to improve the quality of solutions and chaotic perturbation is used to prevent premature convergence. Numerous simulations have been carried out to demonstrate the efficiency of the proposed algorithm.Master of Science (Computer Control and Automation

Spatial Factors Outperform Local Environmental and Geo-Climatic Variables in Structuring Multiple Facets of Stream Macroinvertebrates’ β-Diversity

One of the key targets of community ecology and biogeography concerns revealing the variability and underlying drivers of biodiversity. Most current studies understand biodiversity based on taxonomic information alone, but few studies have shown the relative contributions of multiple abiotic factors in shaping biodiversity based on taxonomic, functional, and phylogenetic information. We collected 179 samples of macroinvertebrates in the Hun-Tai River Basin. We validated the complementarity between the three facets and components of β-diversity using the Mantel test. Distance-based redundancy analysis and variance partitioning were applied to explore the comparative importance of local environmental, geo-climatic, and spatial factors on each facet and component of β-diversity. Our study found that taxonomic and phylogenetic total β-diversity was mainly forced by turnover, while functional total β-diversity was largely contributed by nestedness. There is a strong correlation between taxonomic and phylogenetic β-diversity. However, the correlations of functional with both taxonomic and phylogenetic β-diversity were relatively weak. The findings of variation partitioning suggested that distinct facets and components of macroinvertebrates’ β-diversity were impacted by abiotic factors to varying degrees. The contribution of spatial factors was greater than that of the local environment and geo-climatic factors for taxonomic, functional, and phylogenetic β-diversity. Thus, studying different facets and components of β-diversity allows a clearer comprehension of the influence of abiotic factors on diversity patterns. Therefore, future research should investigate patterns and mechanisms of β-diversity from taxonomic, functional, and phylogenetic perspectives

Current status of clinical application of immunotherapy in the treatment of glioma: A narrative review

Glioma is a common type of brain tumor. Current treatment for glioma includes surgical resection, radiotherapy, chemotherapy, and tumor-treating fields. The application of immunotherapy to treat glioma is still far from satisfactory in the clinic. Here, we review the mechanisms of immunotherapy for glioma (including immune checkpoint inhibitor, chimeric antigen receptor-T-cell, tumor vaccine, and oncolytic virus) and the results of completed clinical trials, and will discuss the current status of immunotherapy and possible future directions

Theaflavin pretreatment ameliorates renal ischemia/reperfusion injury by attenuating apoptosis and oxidative stress in vivo and in vitro

Oxidative stress-induced apoptosis is an important pathological process in renal ischemia/reperfusion injury (RIRI). Theaflavin (TF) is the main active pigment and polyphenol in black tea. It has been widely reported because of its biological activity that can reduce oxidative stress and protect against many diseases. Here, we explored the role of theaflavin in the pathological process of RIRI. In the present study, the RIRI model of 45 min ischemia and 24 h reperfusion was established in C57BL/6 J male mice, and theaflavin was used as an intervention. Compared with the RIRI group, the renal filtration function, renal tissue damage and antioxidant capacity of the theaflavin intervention group were significantly improved, while the level of apoptosis was reduced. TCMK-1 cells were incubated under hypoxia for 48 h and then reoxygenated for 6 h to simulate RIRI in vitro. The application of theaflavin significantly promoted the translocation of p53 from cytoplasm to nucleus, upregulated the expression of glutathione peroxidase 1 (GPx-1) in cells, and inhibited oxidative stress damage and apoptosis. Transfection with p53 siRNA can partially inhibit the effect of theaflavin. Thus, theaflavin exerted a protective effect against RIRI by inhibiting apoptosis and oxidative stress via regulating the p53/GPx-1 pathway. We conclude that theaflavin has the potential to become a candidate drug for the prevention and treatment of RIRI