Group 1 - The core viewpoint of the article emphasizes the significance of multi-sensor fusion perception (MSFP) in embodied AI, highlighting its role in enhancing perception capabilities and decision-making accuracy [5][6][66] - Embodied AI is defined as an intelligent form that utilizes physical entities as carriers to achieve autonomous decision-making and action capabilities in dynamic environments, with applications in autonomous driving and robotic clusters [6][7] - The article discusses the necessity of multi-sensor fusion due to the varying performance of different sensors under different environmental conditions, which can lead to more robust perception and accurate decision-making [7][8] Group 2 - The article outlines the limitations of current research, noting that existing surveys often focus on single tasks or fields, making it difficult for researchers in other related tasks to benefit [12][13] - It identifies challenges at the data level, model level, and application level, including data heterogeneity, temporal asynchrony, and sensor failures [12][66] - The article presents various types of sensor data, including camera data, LiDAR data, and mmWave radar data, detailing their characteristics and limitations [11][13] Group 3 - Multi-modal fusion methods are highlighted as a key area of research, aiming to integrate data from different sensors to reduce perception blind spots and achieve comprehensive environmental awareness [19][20] - The article categorizes fusion methods into point-level, voxel-level, region-level, and multi-level fusion, each with specific techniques and applications [21][29] - Multi-agent fusion methods are discussed, emphasizing the advantages of collaborative perception among multiple agents to enhance robustness and accuracy in complex environments [33][36] Group 4 - Time series fusion is identified as a critical component of MSFP systems, enhancing perception continuity and spatiotemporal consistency by integrating multi-frame data [49][51] - The article introduces query-based time series fusion methods, which have become mainstream due to the rise of transformer architectures in computer vision [53][54] - Multi-modal large language models (MM-LLM) are explored for their role in processing and integrating data from various sources, although challenges remain in their practical application [58][59] Group 5 - The article concludes by addressing the challenges faced by MSFP systems, including data quality, model fusion strategies, and real-world adaptability [76][77] - Future work is suggested to focus on developing high-quality datasets, effective fusion strategies, and adaptive algorithms to improve the performance of MSFP systems in dynamic environments [77][68]