Enhanced Language-guided Robot Navigation with Panoramic Semantic Depth Perception and Cross-modal Fusion

Abstract

Integrating visual observation with linguistic instruction holds significant promise for enhancing robot navigation across unstructured environments and enriches the human-robot interaction experience. However, while panoramic RGB views furnish robots with extensive environmental visuals, current methods significantly overlook crucial semantic and depth cues. This incomplete representation may lead to misinterpretation or inadequate execution of language instructions, thereby impeding navigation performance and adaptability. In this paper, we introduce SEAT, a semantic-depth aware cross-modal transformer model. Our approach incorporates an efficient panoramic multi-type visual encoder to capture comprehensive environmental details. To mitigate the rigidity of feature mapping stemming from the freezing of pre-training encoders, we propose a novel region query pre-training task. Additionally, we leverage an improved dual-scale cross-modal transformer to facilitate the integration of instructions, topological memory, and action prediction. Extensive experiments on three language-guided robot navigation datasets demonstrate the efficacy of our model, achieving competitive navigation success rates with fewer parameters and computational load. Furthermore, we validate SEAT’s effectiveness in real-world scenarios by deploying it on a mobile robot across various environments. The code is available at https://github.com/CrystalSixone/SEAT.