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OpenStreetMap (OSM), an online and versatile source of volunteered geographic information (VGI), is widely used for human self-localization by matching nearby visual observations with vectorized map data. However, due to the divergence in modalities and views, image-to-OSM (I2O) matching and localization remain challenging for robots, preventing the full utilization of VGI data in the unmanned ground vehicles and logistic industry. Inspired by the fact that the human brain relies on geometric and semantic understanding of sensory information for spatial localization tasks, we propose the OSMLoc in this paper. OSMLoc is a brain-inspired single-image visual localization method with semantic and geometric guidance to improve accuracy, robustness, and generalization ability. First, we equip the OSMLoc with the visual foundational model to extract powerful image features. Second, a geometry-guided depth distribution adapter is proposed to bridge the monocular depth estimation and camera-to-BEV transform. Thirdly, the semantic embeddings from the OSM data are utilized as auxiliary guidance for image-to-OSM feature matching. To validate the proposed OSMLoc, we collect a worldwide cross-area and cross-condition (CC) benchmark for extensive evaluation. Experiments on the MGL dataset, CC validation benchmark, and KITTI dataset have demonstrated the superiority of our method. Code, pre-trained models, CC validation benchmark, and additional results are available on: https://github.com/WHU-USI3DV/OSMLoc, Comment: 15 pages, technical report

Precise and rapid delineation of sharp boundaries and robust semantics is essential for numerous downstream robotic tasks, such as robot grasping and manipulation, real-time semantic mapping, and online sensor calibration performed on edge computing units. Although boundary detection and semantic segmentation are complementary tasks, most studies focus on lightweight models for semantic segmentation but overlook the critical role of boundary detection. In this work, we introduce Mobile-Seed, a lightweight, dual-task framework tailored for simultaneous semantic segmentation and boundary detection. Our framework features a two-stream encoder, an active fusion decoder (AFD) and a dual-task regularization approach. The encoder is divided into two pathways: one captures category-aware semantic information, while the other discerns boundaries from multi-scale features. The AFD module dynamically adapts the fusion of semantic and boundary information by learning channel-wise relationships, allowing for precise weight assignment of each channel. Furthermore, we introduce a regularization loss to mitigate the conflicts in dual-task learning and deep diversity supervision. Compared to existing methods, the proposed Mobile-Seed offers a lightweight framework to simultaneously improve semantic segmentation performance and accurately locate object boundaries. Experiments on the Cityscapes dataset have shown that Mobile-Seed achieves notable improvement over the state-of-the-art (SOTA) baseline by 2.2 percentage points (pp) in mIoU and 4.2 pp in mF-score, while maintaining an online inference speed of 23.9 frames-per-second (FPS) with 1024x2048 resolution input on an RTX 2080 Ti GPU. Additional experiments on CamVid and PASCAL Context datasets confirm our method's generalizability. Code and additional results are publicly available at https://whu-usi3dv.github.io/Mobile-Seed/., Comment: Accepted by IEEE Robotics and Automation Letters (RA-L) 2024. Code, pre-trained models and additional results are available at https://whu-usi3dv.github.io/Mobile-Seed/

Image-to-point cloud (I2P) registration is a fundamental task for robots and autonomous vehicles to achieve cross-modality data fusion and localization. Current I2P registration methods primarily focus on estimating correspondences at the point or pixel level, often neglecting global alignment. As a result, I2P matching can easily converge to a local optimum if it lacks high-level guidance from global constraints. To improve the success rate and general robustness, this paper introduces CoFiI2P, a novel I2P registration network that extracts correspondences in a coarse-to-fine manner. First, the image and point cloud data are processed through a two-stream encoder-decoder network for hierarchical feature extraction. Second, a coarse-to-fine matching module is designed to leverage these features and establish robust feature correspondences. Specifically, In the coarse matching phase, a novel I2P transformer module is employed to capture both homogeneous and heterogeneous global information from the image and point cloud data. This enables the estimation of coarse super-point/super-pixel matching pairs with discriminative descriptors. In the fine matching module, point/pixel pairs are established with the guidance of super-point/super-pixel correspondences. Finally, based on matching pairs, the transform matrix is estimated with the EPnP-RANSAC algorithm. Experiments conducted on the KITTI Odometry dataset demonstrate that CoFiI2P achieves impressive results, with a relative rotation error (RRE) of 1.14 degrees and a relative translation error (RTE) of 0.29 meters, while maintaining real-time speed.Additional experiments on the Nuscenes datasets confirm our method's generalizability. The project page is available at \url{https://whu-usi3dv.github.io/CoFiI2P}., Comment: Accepted by IEEE RA-L 2024. Code, pretrained models and additional results are available at: https://whu-usi3dv.github.io/CoFiI2P