Place recognition is a cornerstone of vehicle navigation and mapping, which is pivotal in enabling systems to determine whether a location has been previously visited. This capability is critical for tasks such as loop closure in Simultaneous Localization and Mapping (SLAM) and long-term navigation under varying environmental conditions. This survey comprehensively reviews recent advancements in place recognition, emphasizing three representative methodological paradigms: Convolutional Neural Network (CNN)-based approaches, Transformer-based frameworks, and cross-modal strategies. We begin by elucidating the significance of place recognition within the broader context of autonomous systems. Subsequently, we trace the evolution of CNN-based methods, highlighting their contributions to robust visual descriptor learning and scalability in large-scale environments. We then examine the emerging class of Transformer-based models, which leverage self-attention mechanisms to capture global dependencies and offer improved generalization across diverse scenes. Furthermore, we discuss cross-modal approaches that integrate heterogeneous data sources such as Lidar, vision, and text description, thereby enhancing resilience to viewpoint, illumination, and seasonal variations. We also summarize standard datasets and evaluation metrics widely adopted in the literature. To the best of our knowledge, no prior survey has systematically reviewed visual, LiDAR, and cross-modal place recognition concurrently. This work thus resolves a critical gap in existing literature dominated by single-modality studies. Finally, we identify current research challenges and outline prospective directions, including domain adaptation, real-time performance, and lifelong learning, to inspire future advancements in this domain. The unified framework of leading-edge place recognition methods, i.e., code library, and the results of their experimental evaluations are available at https://github.com/CV4RA/SOTA-Place-Recognitioner.

Environment description-based vehicle-scene interaction for vehicle localization in large-scale point cloud maps constructed through multi-sensor systems is critically significant for the advancement of large-scale intelligent transportation systems, such as delivery vehicles operating in the ‘last mile’. However, current vehicle-scene interaction encounter challenges due to the inability of point cloud encoders to effectively capture local details and long-range spatial relationships and a significant modality gap between text and point cloud representations. To address these challenges, we present Des4Pos, a novel two-stage text-driven 3D Lidar place recognition framework. In the coarse stage, the point-cloud encoder utilizes the Multi-scale Fusion Attention Mechanism (MFAM) to enhance local geometric features, followed by a bidirectional Long Short-Term Memory (LSTM) module to strengthen global spatial relationships. Concurrently, the Stepped Text Encoder (STE) integrates cross-modal prior knowledge from CLIP and aligns text and point-cloud features using this prior knowledge, effectively bridging modality discrepancies. In the fine stage, we introduce a Cascaded Residual Attention (CRA) module to fuse cross-modal features and predict relative localization offsets, thereby achieving greater localization precision. Experiments on the KITTI360Pose test set demonstrate that Des4Pos achieves state-of-the-art performance in text-to-point-cloud place recognition at the expense of a slight increase in the runtime. Specifically, it attains a top-1 accuracy of 40% and a top-10 accuracy of 77% under a 5-meter radius threshold, surpassing the best previous sota method Text2Loc by 8% and 7%, respectively. Our code and datasets are publicly available at https://github.com/nuozimiaowu/Des4Pos

Large-field-of-view (FOV) and high-resolution imaging have always been the goals pursued by imaging technology. A scale-adaptive high-resolution imaging architecture is established using a rotating-prism-embedded variable-boresight camera. By planning to prism motion, the multi-view images with rich information are combined to form a large-scale FOV image. The boresight is guided towards the region of interest (ROI) in the combined FOV to reconstruct super-resolution (SR) images with the desired information. A novel distortion correction method is proposed using virtual symmetrical prisms with rotation angles that are complementary. Based on light reverse tracing, the dispersion induced by monochromatic lights with different refractive indices can be eliminated by accurate pixel-level position compensation. For resolution enhancement, we provide a new scheme for SR imaging consisting of the residual removal network and information enhancement network by multi-view image fusion. The experiments show that the proposed architecture can achieve both large-FOV scene imaging for situational awareness and SR ROI display to acquire details, effectively perform distortion and dispersion correction, and alleviate the occlusion to a certain extent. It also provides higher image clarity compared to the traditional SR methods and overcomes the problem of balancing large-scale imaging and high-resolution imaging.

Place recognition is a cornerstone of vehicle navigation and mapping, which is pivotal in enabling systems to determine whether a location has been previously visited. This capability is critical for tasks such as loop closure in Simultaneous Localization and Mapping (SLAM) and long-term navigation under varying environmental conditions. This survey comprehensively reviews recent advancements in place recognition, emphasizing three representative methodological paradigms: Convolutional Neural Network (CNN)-based approaches, Transformer-based frameworks, and cross-modal strategies. We begin by elucidating the significance of place recognition within the broader context of autonomous systems. Subsequently, we trace the evolution of CNN-based methods, highlighting their contributions to robust visual descriptor learning and scalability in large-scale environments. We then examine the emerging class of Transformer-based models, which leverage self-attention mechanisms to capture global dependencies and offer improved generalization across diverse scenes. Furthermore, we discuss cross-modal approaches that integrate heterogeneous data sources such as Lidar, vision, and text description, thereby enhancing resilience to viewpoint, illumination, and seasonal variations. We also summarize standard datasets and evaluation metrics widely adopted in the literature. Finally, we identify current research challenges and outline prospective directions, including domain adaptation, real-time performance, and lifelong learning, to inspire future advancements in this domain. The unified framework of leading-edge place recognition methods, i.e., code library, and the results of their experimental evaluations are available at https://github.com/CV4RA/SOTA-Place-Recognitioner.