系统梳理雷达与相机融合在目标检测跟踪中的方法与进展
Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey
- 从传感器标定到特征融合的全流程技术解析
- 提出涵盖检测与跟踪的雷达相机融合分类体系
- 适合自动驾驶、智能感知等领域的研究者参考
多模态融合对于复杂环境下可靠的目标检测与跟踪至关重要。利用异构模态信息的协同效应，使感知系统具备更全面、鲁棒和精确的性能。作为无线视觉协同的核心议题，雷达-相机融合因其广泛适用性、互补性和兼容性，催生了众多前瞻性研究方向。然而，目前仍缺乏专注于雷达与相机深度融合在目标检测与跟踪中应用的系统性综述。为此，本文旨在全面回顾雷达-相机融合的全貌。首先，阐述其基本原理、方法与应用场景；其次，深入探讨传感器标定、模态表征、数据对齐及融合操作等关键技术；进一步，构建涵盖雷达与相机技术背景下目标检测与跟踪相关研究主题的详细分类体系；最后，展望该领域的发展趋势，指出未来潜在的研究方向。
原文摘要 · Abstract (English)
Multi-modal fusion is imperative to the implementation of reliable object detection and tracking in complex environments. Exploiting the synergy of heterogeneous modal information endows perception systems the ability to achieve more comprehensive, robust, and accurate performance. As a nucleus concern in wireless-vision collaboration, radar-camera fusion has prompted prospective research directions owing to its extensive applicability, complementarity, and compatibility. Nonetheless, there still lacks a systematic survey specifically focusing on deep fusion of radar and camera for object detection and tracking. To fill this void, we embark on an endeavor to comprehensively review radar-camera fusion in a holistic way. First, we elaborate on the fundamental principles, methodologies, and applications of radar-camera fusion perception. Next, we delve into the key techniques concerning sensor calibration, modal representation, data alignment, and fusion operation. Furthermore, we provide a detailed taxonomy covering the research topics related to object detection and tracking in the context of radar and camera technologies.Finally, we discuss the emerging perspectives in the field of radar-camera fusion perception and highlight the potential areas for future research.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。