Embodied navigation in unknown environments presents the significant challenge of integrating tasks with multimodal goals into a unified framework. In this paper, we propose the Multimodal Semantic Navigation on Relative Metric Intention Graph (Ms. NAMI), a framework that integrates various navigation tasks with multimodal goals based on a relative topo-metric intention graph. A reinforcement learning based policy with a concise action space, consisting of frontier nodes and intention nodes, is designed to guide the agent to select reasonable sub-goals. A sparse reward design is introduced to reduce bias during training. Additionally, several engineering optimizations are implemented to enhance overall performance. The experimental results indicate that our method can achieve robust navigation performance in a variety of unknown environments.

Image-goal navigation in unknown environments with sensor error is of considerable difficulty for autonomous robots. In this paper, we propose a drift-resisting topo-metric graph to map the environment and localize the robot using only relative poses. The error-sharing mechanism under this representation effectively reduces the impact of accumulated drifts commonly encountered in navigation tasks. A Reinforcement Learning based policy was proposed for sub-goal selection on this topo-metric graph, which improves navigation efficiency by handling task-driven features taking both image correlation and topological layout into account. We adopt a modular system design with this map representation and graph policy, leaving the low-level motion planning problems to classical controllers for better stability and generalizability. Experimental results demonstrate that our method can achieve robust navigation performance in a variety of unknown environments and even 50% higher success rate over existing methods in complex environments with odometry drift.