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.

Visual place recognition (VPR) is a challenging issue for robotics and autonomous systems, focusing on utilizing visual information for robot localization. Currently, hierarchical architecture is being employed by growing works, which embraces RANSAC-based geometric verification for re-ranking. However, RANSAC is time-consuming and only employs geometric information while neglecting other potential information that could be useful for re-ranking. Here we propose a fast position consistency via local patch (PCLP) algorithm to take the position of task-relevant patch-descriptor into account. Without training, it only costs little time but performs better than other re-ranking methods that rely on geometric consistency verification. In this paper, we present a unified place recognition framework that incorporates an aggregation module to extract global features for retrieval and a PCLP validation module to filter local patch for reranking. Meanwhile, we propose a RANSAC-based tightly coupled learning (R-TCL) strategy to discover the best positive sample for training robust models. Unlike common sample mining methods, we introduce RANSAC into the sample mining process, achieving trade-off between efficiency and accuracy. Due to improved positive sample mining strategy and novel position validation module, our model is named as Pos2VPR. Remarkably, Pos2VPR outperforms state-of-the-art methods on four major datasets with extremely short running time.