IVLMap: Instance-Aware Visual Language Grounding for Consumer Robot Navigation
This addresses a specific limitation in consumer robot navigation for tasks requiring precise object instance and attribute distinctions, representing an incremental advancement over prior semantic mapping approaches.
The paper tackles the challenge of instance-level and attribute-level navigation in Vision-and-Language Navigation (VLN) by proposing IVLMap, an instance-aware visual language mapping method that integrates with large language models, achieving a 14.4% average improvement in navigation accuracy in simulations.
Vision-and-Language Navigation (VLN) is a challenging task that requires a robot to navigate in photo-realistic environments with human natural language promptings. Recent studies aim to handle this task by constructing the semantic spatial map representation of the environment, and then leveraging the strong ability of reasoning in large language models for generalizing code for guiding the robot navigation. However, these methods face limitations in instance-level and attribute-level navigation tasks as they cannot distinguish different instances of the same object. To address this challenge, we propose a new method, namely, Instance-aware Visual Language Map (IVLMap), to empower the robot with instance-level and attribute-level semantic mapping, where it is autonomously constructed by fusing the RGBD video data collected from the robot agent with special-designed natural language map indexing in the bird's-in-eye view. Such indexing is instance-level and attribute-level. In particular, when integrated with a large language model, IVLMap demonstrates the capability to i) transform natural language into navigation targets with instance and attribute information, enabling precise localization, and ii) accomplish zero-shot end-to-end navigation tasks based on natural language commands. Extensive navigation experiments are conducted. Simulation results illustrate that our method can achieve an average improvement of 14.4\% in navigation accuracy. Code and demo are released at https://ivlmap.github.io/.