Core Insights - The article provides a comprehensive review of semantic mapping methods in indoor embodied AI, covering traditional methods to the latest deep learning advancements [4][6] - It proposes a classification framework based on map structure and semantic encoding to help researchers understand and compare different methods [4][7] - The article identifies current challenges in the semantic mapping field, such as high memory demands and low computational efficiency, and suggests future research directions [4][6] Research Background - Semantic maps are crucial for agents (both physical robots and virtual systems) to operate in complex, unstructured environments, linking perception with reasoning and decision-making [6] - The importance of semantic maps has grown in robotics and embodied AI, especially in open-world environments like autonomous driving and search and rescue [6] - Existing reviews mainly focus on the application of semantic maps in downstream tasks, while this article emphasizes the underlying map representations [6] Classification Framework - The article categorizes semantic mapping methods based on two dimensions: map structure (e.g., spatial grids, topological maps, dense geometric maps) and semantic encoding (explicit vs. implicit features) [7] - This classification aims to unify different research directions, highlight trade-offs between representations, and propose key challenges and opportunities in semantic mapping [7] Embodied Tasks - Embodied tasks involve agents perceiving and interacting with their environment through sensors and actuators, requiring an understanding of the world and meaningful actions [9] - The evolution of robotics has progressed from simple collision avoidance to complex perception, mapping, and manipulation capabilities [9] - Current trends include uncertainty-aware planning and task planning in dynamic environments, with a rise in bird's-eye view representations for tasks like detection and trajectory prediction [10] SLAM and Semantic SLAM - SLAM is a core concept in robotics closely related to semantic mapping, enabling robots to perceive their environment and simultaneously localize themselves while building maps [12][18] - Semantic SLAM enhances traditional SLAM by integrating semantic information into spatial maps, bridging the gap between perception and task-level reasoning [22] System Design Strategies - When designing embodied agent systems, a fundamental architectural choice must be made between end-to-end learning and modular pipelines, impacting how maps are constructed and utilized [20] - End-to-end methods map raw sensory input directly to actions using a single neural network, while modular systems break tasks into interpretable components [21][23] Semantic Maps - Semantic maps contain both geometric and high-level semantic information about the environment, aiding agents in complex tasks like navigation and object manipulation [25] - Various map structures exist, including spatial grid maps, topological maps, dense geometric maps, and hybrid maps, each with unique advantages and disadvantages [29][39][46] Encoding Types - Maps can store information through explicit encoding (clear semantic meaning) or implicit encoding (learned feature representations) [28][67] - Explicit encoding is beneficial for tasks requiring clear semantic understanding, while implicit encoding allows for flexibility in recognizing unseen object categories [70][72] Future Directions - The article suggests developing open vocabulary maps and task-agnostic representations as future research directions to address current challenges in semantic mapping [4][6]