LiDAR point clouds 3D semantic segmentation enables efficient and accurate environmental sensing for intelligent vehicles and autonomous robots, greatly advancing these domains. Existing advanced methods that use 3D sparse convolutional often suffer from a small Effective Receptive Field (ERF), which limits context sensing and challenging highperformance segmentation. Building on this observation, we propose MDC-Seg for efficient ERF enlargement. We design Multi-directional Convolution (MDConv), which simultaneously performs sparse feature encoding on the Bird's Eye View (BEV) and Range View (RV) planes to enlarge the ERF of 3D sparse convolution. To enhance feature fusion in MDConv, we introduce an attention mechanism and design an efficient multifeature fusion (EMFF) module suitable for both 3D and 2D sparse features. To improve segmentation accuracy, we design a point-voxel constraint (PVC) module to handle edge voxels containing multiple point cloud categories, optimizing the final inference results. These modules add minimal memory and inference time but significantly improve performance compared to the baseline. Extensive experiments on the SemanticKITTI benchmark demonstrate MDC-Seg's excellent performance, with supplementary tests on nuScenes further confirming its superiority by yielding good results. The source code is available at https://github.com/OYgreat-river/MDC-Seg.

Modern robotic systems are increasingly deployed in complex and diverse environments, and reliable localization under challenging conditions becomes crucial for the safe and efficient operation of these systems. The odometry based on LiDAR is prone to system collapse caused by computational divergence under conditions of aggressive motion and information deficiency in spatial geometry. To enhance the robustness of systems in challenging scenes, this work proposes LA-LIO, robust localizability-aware LiDAR inertial odometry. It mainly consists of three parts. Firstly, this paper presents a LiDAR degeneration detection method that enables stable degeneration assessment. Secondly, a method for segmenting LiDAR point clouds is proposed to alleviate the issue of excessive distortion in point clouds under aggressive motion scenes. The last is an Errors State Kalman Filter (ESKF) method with adaptive weights to utilize the existing spatial information as much as possible to improve the stability of the system in degenerated scenarios. The proposed method is evaluated and compared in multiple experiments, demonstrating the performance and reliability improvements of this approach in challenging environments.

Simultaneous Localization and Mapping (SLAM) is widely used in applications such as robotics and autonomous driving, with methods involving multi-sensor fusion demonstrating excellent performance. However, they simply reject dynamic features and ignore the mutual benefits of self and dynamic objects, which greatly limits their application in actual high-dynamic scenes. To address this issue, we propose ESO-SLAM, a tightly-coupled system for simultaneous self and multi-object pose estimation achieved through sensor fusion. This system employs a multi-probability fusion tracker based on filter to establish more robust object-level data association. Building upon this, we introduce a method that combines 3D Kalman filter velocity priors and camera optical flow decoupling for dynamic point cloud removal, aiming at improving the accuracy of self-pose estimation in odometry. Finally, we jointly refine the poses of the robot and objects using multiple constraint factors within our proposed framework. Experimental results on the KITTI raw dataset demonstrate that our approach achieves better pose accuracy for both self and tracked objects compared to baseline and state-of-the-art techniques. Furthermore, the proposed method exhibits feasibility in real-time performance to ensure its practical application value.

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.

Cross-view geo-localization refers to recognizing images of the same geographic target obtained from different platforms (such as drone-view, satellite-view and ground-view). However, cross-view geo-localization is challenging as image capture using different platforms coupled with extreme viewpoint variations can cause significant changes to the visual image content. Existing methods mainly focus on mining the fine-grained features or the contextual information in neighboring areas, but ignore the complete information of the entire image and the association of contextual information of adjacent regions. Therefore, a multi-branch joint representation learning network model based on information fusion strategies is proposed to solve this cross-view geo-localization problem. Firstly, we obtain feature information from the image through global information fusion branch and local information fusion branch to help the network learn the discernable information in the different images. In addition, a local-guided-global information fusion branch is introduced to make local information assist global features to enhance the learning of potential information in the images. Secondly, we introduced different information fusion strategies in each branch to increase the extraction of contextual information through expanding the global receptive field, thus improving the performance of the model. Finally, a series of experiments is carried out on four prevailing benchmark datasets, namely University-1652, SUES-200, CVUAS and CVACT datasets. The quantitative comparisons from the experiments clearly indicate that the proposed network framework has great performance. For example, compared with some state-of-the-art methods, the quantitative improvements of the R@1 and AP on the University-1652 datasets are 1.91%, 2.18% and 1.55%, 2.99% in both tasks, respectively.