同济大学最新！多模态感知具身导航全面综述

Core Insights - The article presents a comprehensive analysis of multimodal navigation methods, emphasizing the integration of various sensory modalities such as visual, audio, and language processing to enhance navigation capabilities [4][32]. Group 1: Research Background - Goal-oriented navigation is a fundamental challenge in autonomous systems, requiring agents to navigate complex environments to reach specified targets. Over the past decade, navigation technology has evolved from simple geometric path planning to complex multimodal reasoning [7][8]. - The article categorizes goal-oriented navigation methods based on reasoning domains, revealing commonalities and differences among various tasks, thus providing a unified framework for understanding navigation methods [4]. Group 2: Navigation Tasks - Navigation tasks have increased in complexity, evolving from simple point navigation (PointNav) to more complex multimodal paradigms such as ObjectNav, ImageNav, and AudioGoalNav, each requiring different levels of semantic understanding and reasoning [8][12]. - The formal definition of navigation tasks is framed as a decision-making process where agents must reach specified goals in unknown environments through a series of actions [8]. Group 3: Datasets and Evaluation - The Habitat-Matterport 3D (HM3D) dataset is highlighted as the largest collection, encompassing 1,000 reconstructed buildings and covering 112.5k square meters of navigable area, with varying complexities across other datasets like Gibson and Matterport3D [9]. - Evaluation metrics for navigation tasks include success rate (SR), path length weighted success rate (SPL), and distance-related metrics, which assess the efficiency and effectiveness of navigation strategies [14]. Group 4: Methodologies - Explicit representation methods, such as ANM and LSP-UNet, construct and maintain environmental representations to support path planning, while implicit representation methods, like DD-PPO and IMN-RPG, encode spatial understanding without explicit mapping [15][16]. - Object navigation tasks are modularly approached, breaking down the task into mapping, strategy, and path planning, with methods like Sem-EXP and PEANUT focusing on semantic understanding [17]. Group 5: Challenges and Future Work - Current challenges in multimodal navigation include the effective integration of sensory modalities, the transfer from simulation to real-world applications, and the development of robust multimodal representation learning methods [31][32]. - Future work is suggested to focus on enhancing human-robot interaction, developing balanced multimodal representation learning methods, and addressing the computational efficiency of navigation systems [32].